Background: Acupuncture is a common treatment for dysmenorrhea. However, there is still uncertainty regarding the efficacy of acupuncture for patients with dysmenorrhea and the dose-response relationship between acupuncture and dysmenorrhea. Objectives This study aimed to explore the relationship between acupuncture and pain relief in dysmenorrhea patients based on previously published randomized controlled trials (RCTs). Search strategy Eight databases including English, Chinese database, were searched from inception until May 2023. Selection criteria RCTs Comparing Acupuncture with Sham Acupuncture, Western medicine, or no intervention for treatment. Data collection and analysis The study abstraction and quality assessment of all studies were undertaken following the detailed descriptions of these categories as described in the Cochrane Handbook for Systematic Reviews of Interventions. Main outcome The outcome measures were menstrual pain intensity on numerical rating scale scores (VAS). Results The meta-analysis of pain change scores indicated that acupuncture had beneficial effect [n = 492; SMD = −2.29, 95% CI (-3.57, -1.01); I 2 = 94.7%]. The meta-regression model and the results indicated that acupuncture sessions were associated with greater reducing menstrual pain. A V-shaped association between acupuncture sessions with VAS scores was presented: After 10 acupuncture sessions, the pain reduction was optimal at 2.06 (95% CI: 1.46–2.66), with a difference of −4.06 (95% CI: −4.73 to −3.39). Conclusion: Acupuncture can effectively relieve dysmenorrhea. A dose-effect relationship was found between the number of acupuncture sessions and VAS scores. 10 acupuncture sessions may associate with optimal clinical response.

Background Management of suspected large for gestational age (LGA) fetuses remains unclear because the lack of accuracy of ultrasound estimated fetal weight. Objectives To assess the methodology of studies reporting 2D ultrasound assessment of fetal soft tissues with the aim of improving the screening of LGA fetuses. Search strategy This was a systematic review of observational studies. A search for relevant articles was performed in MEDLINE, EMBASE, CINAHL, Web of Science (from inception to 31 January 2022), and references of the retrieved articles. Two authors independently selected studies and assessed the risk of bias and extracted the data. Selection criteria Studies on fetal soft tissues measurements used alone or in combination to create a new estimated fetal weight (EFW) formula, to improve the screening for LGA fetuses. Data collection and analysis Studies were scored using a predefined set of independently agreed methodological criteria and an overall quality score was assigned for study design, statistical analysis, and reporting methods. Main results Thirty-two studies met the inclusion criteria (ten for a new EFW algorithm and twenty-two for an isolated parameter used as cut-off for LGA fetuses detection). The highest potential bias were noted in the following fields: sample size estimation, diabetes description, report of perinatal outcomes, number of measurements taken, and multicenter study. Only eight out of thirty-two studies scored over 70% in design quality. Conclusions There is a need for the standardization of methodologies for soft fetal tissues measurements and for the development of reference standards, which can be correctly interpreted and applied in clinical practice. We propose a set of suggestions for this purpose.

Background: Prior studies have demostrated that the male factor is present in 40-50% of couples with fertility problems. Because of this, and due to the increase in demand for assisted reproduction treatments, research on the etiology of male infertility has increased in recent years. Recent studies highlight how certain modifiable factors, especially dietary habits, play an important role in male fertility. Objective: The aim of this study was to examine the influence of the Mediterranean diet on seminal quality in men of reproductive age. For this purpose, a systematic review of the literature was carried out following the PRISMA method. Search strategy, selection criteria, data collection and analysis: The search strategy was designed to identify full-text, published articles and included MeSH terms and the terms title and abstract. The keywords used were “Mediterranean diet”, “semen” , “quality”. 10 articles with 2032 men were included. Nutritional status was assessed through weight, height and BMI. Dietary habits were evaluated through different indexes and food frequency questionnaires and finally semen quality was evaluated by measuring sperm concentration and motility (progressive and non-progressive). Main results: In 6 (60%) of the included articles a positive relationship between adherence to the Mediterranean diet and semen quality was demonstrated, in 2 (20%) of the articles no association was found and finally, in 2 (20%) of the included articles the relationship between dietary patterns typical of DM and semen quality was evaluated. Conclusion: Dietary habits influence semen quality. Adherence to the Mediterranean diet can improve male reproductive health as it is a diet with antioxidant and anti-inflammatory effects. Key words: Semen, quality, Mediterranean diet, fertility.

Left ventricular diverticulum（LVD） represents an rare congenital anomaly. Subaortic diverticulum (SD) is a special type of LVD. It is consisting of a localized outpouching from the free wall of the left ventricular outflow tract and can cause aortic regurgitant，with a male to female ratio of 16:1. SD should be distinguished from aortic dissection in addition to aortic sinus aneurysm. We report a female case of subvalvular diverticulum with aortic dysplasia misdiagnosed as a major artery dissection by bedside echocardiography. The patient had mild symptoms and underwent diverticular repair and aortic valve replacement.

multi-particle system under a proximity-based communication topology on the Lorentz group SO(1,1). With the help of the Riemannian manifold structure on the Lorentz group SO(1,1), we design a rendezvous controller for particles by using the covariant derivative, parallel transport, and Riemannian logarithm map on SO(1,1). We further show that the proposed controller can guarantee that the distance between each particle converges to zero and maintain the connectivity of the communication topology if the initial network is connected. Several numerical simulations are provided to verify and illustrate the theoretical results.

Nitesh Kumar MD, MBA1, Payal Bai MD2, Shilpa MD3, Shelby Johnson, MD, MPH4, Abhishek Singh MD51Clinical Medicine, Windsor University School of Medicine, Chicago, IL

Despite offering remarkable advantages as solvents, double salt ionic liquids (DSILs) have been scarcely studied for extractive dearomatization from hydrocarbons as well as many other applications, thus urging a theoretical guidance method. In this work, a systematic framework combining the rational screening-validation and mechanistic analysis is proposed for tailoring DSILs for the o-xylene/n-octane separation. From an initial pool of commercially available ionic liquids (ILs), key thermodynamic properties of paired DSILs are predicted by COSMO-RS while their important physical properties are estimated from those of corresponding parent ILs (retrieved from experimental database or predicted by a deep learning model). Promising DSILs are tested by liquid-liquid equilibrium experiments, wherein the ion ratio-effect is also evaluated. The mechanism underlying the tunability of DSIL thermodynamic properties is disclosed by means of quantum chemistry calculation and molecular dynamics simulation. This work can be a valuable reference for guiding the design of DSILs for diverse applications.

Various examples show that stratified columns can improve the transport performance of particle packings. However, to date, there is no universal approach to design these packings to yield optimal performance. This study proposes a novel model-based method for designing particle packings in which mass transfer occurs between a liquid phase and a stationary phase using optimal control theory. The primary objective is to provide a general design strategy that is applicable across different unit operations in chemical, pharmaceutical, and food applications. Optimal control is utilized to determine the optimal particle diameter as a function of the axial position within the column. We demonstrate the approach using two case studies and three different optimization criteria. Numerical results indicate that the proposed method is highly effective, e.g., the solvent demand is reduced by up to 32.47 %. Moreover, the optimally graded packing yields a significantly sharper breakthrough curve of an adsorption column.

Modular CO2 capture plants can help reducing the cost of deploying capture systems across the globe. However, the CO2 variability and model uncertainty represent operational challenges to capture CO2 from different sources. This work proposes a framework for analyzing the optimal plant design considering different flue gas sources. We show a methodology to generate large data sets from optimization runs using rigorous models in Aspen Plus®. The efficiency of the approach allows its application to large-scale optimization problems (<45,000 variables and equations), with an average CPU time per run of 176 s. We also build surrogate models (SMs) for the capital and operating costs of the capture plants, considering the uncertainty of parameters. We propose an iterative procedure to generate SMs using ALAMO, rejecting SMs with high uncertainty in the estimated parameters. In this way, we obtain SMs with suitable bias-variance tradeoffs, allowing their application to optimization problems under uncertainty.

Hepatotoxicity refers to the deterioration in liver function brought about by pharmacological chemical side effects. Hepatotoxicity is frequently brought on by a wide range of pharmaceutical drugs and environmental pollutants. Hepatotoxins are the substance that harms the liver. Hepatotoxic drugs and chemicals have the potential to damage the liver through several mechanisms, including mitochondrial dysfunction, causing the death of the liver cells, and disrupting liver metabolism. Some hepatotoxic drugs and chemicals, such as carbon tetrachloride, paracetamol, mercury, cadmium, erythromycin, ranitidine, etc causing hepatic damage by different ways. Hepatotoxicity is steadily rising in occurrence and is becoming a major cause of death on a global scale. Although there are numerous allopathic and traditional therapies that provide hepatoprotection, treating chronic liver disease remains difficult for medical practitioners. Rodents are frequently employed in the lab for inducing hepatotoxicity for this reason. Hepatic toxicity is typically induced by non-invasive techniques such as high-fat diets, alcohol, radiation, toxic medications (NSAIDs, antibiotics, and chemotherapeutic treatments), harmful metals (mercury, arsenic, lead, and cadmium), and harmful substances (CCL4, thioacetamide, aflatoxin B1, etc.).Portal vein ligation and bile duct ligation are two common invasive procedures.

This manuscript presents a pioneering application of Newton's three laws and the principles of relativity to the study of fundamental theories in economics. By analogizing desires, utility, and prices to physical concepts, and integrating these concepts into the theoretical framework of natural sciences using mathematical language, the author has constructed a binary system and a ternary system for economic analysis. Based on this foundation, the laws of momentum conservation and energy conservation in social sciences are derived on a mathematical level. Subsequently, the characteristics of free markets and non-free markets are analyzed according to the hypotheses of momentum conservation and energy conservation in social sciences. This manuscript argues that the fundamental theoretical principles in social sciences, represented by economics, are unified with those in natural sciences, represented by physics. The mathematical explanation of supply and demand in social sciences presented in this manuscript offers a perfect integration of the theoretical foundations of economics into the framework of physical principles. This opens up new research areas for future studies in social sciences.

The hypersensitive response (HR) characterizes monogenic qualitative resistance traits in several pathosystems. Knowledge on its role in plant resistance to insects is so far limited to a few resistance ( R) gene-based resistances against piercing-sucking insects. Egg deposition by cabbage white butterflies ( Pieris spp.), pests of cabbage crops ( Brassica spp.), can trigger an HR-like cell death, which reduces egg survival and represents an effective plant resistance trait before feeding damage occurs. Here, we identified natural variation of HR-like to Pieris egg deposition in the black mustard ( B. nigra L.) and performed genetic mapping. HR-like segregated as a Mendelian trait and a single dominant locus on chromosome B3, named PEK ( P ieris e gg- k illing) was identified. In the ~50 kb region, eleven candidate genes, are located, including a cluster of genes encoding intracellular receptor proteins, TIR-NBS-LRR (TNLs). The PEK locus was found to be highly polymorphic between the parental accessions of our mapping populations and among B. nigra reference genomes. Our study is the first that identifies a single locus potentially involved in HR-like cell death induced by insect eggs. Further fine-mapping, comparative genomics and validation of the PEK locus will shed light on the role of TNL receptors in egg-induced HR-like cell death.

To detect weak targets via radar communication integrated signal, a method for constructed step-LFM (CStep-LFM) signal is proposed based on quadrature amplitude modulation-orthogonal frequency division multiplexing (QAM-OFDM) echoes. Compared with several existing integrated signals, the analyses and the simulation results show that CStep-LFM signal not only improves the detection performance of weak targets but also has a better communication performance.

In fatigue life prediction, many models fail to account for the impact of various factors, such as small load strengthening effect, loading sequence and real-time damage state. In this paper, a new nonlinear fatigue life prediction model has been developed, which a load action factor is introduced into Ye's toughness dissipation energy model. The action factor combines the small load stress ratio, stress amplitude ratio, and fatigue real-time damage function into an exponential function with multiple powers. This model effectively considers the interaction between loads, loading sequence, real-time damage and the small load strengthening effect. The modified fatigue life prediction model has been tested using data of commonly metal materials, such as 45-gauge steel and 16Mn, under two or more variable amplitude loads. The comparison between experimental and predicted values shows that the new nonlinear fatigue life prediction model can more accurately predict the remaining fatigue life for different materials under multistage variable amplitude loading. Moreover, the model does not require additional parameters and can be easily implemented in practical engineering applications.

This research scrutinizes the implementation of Senior and Swailes’ Organizational Development (OD) model in managing medication for elderly patients within a large organization. Utilizing a mixed-methods approach, it highlights the crucial role of comprehensive planning, potent communication, and stakeholder engagement in executing successful change. The study identifies common barriers such as resource limitations and employee resistance and offers solutions to navigate these challenges. It further illuminates the advantages of the OD model and points out potential areas for future research, including the effect of varied OD models on organizational performance, and leadership’s impact in fostering change initiatives. The research advocates the importance of customizing OD initiatives according to an organization’s unique needs, coupled with continual assessment and adjustments for enduring success.

Biodiesel production is increasing worldwide to supply a demand for renewable, environmentally-friendly fuels. In this sense, microbial oils appeared as potential feedstock for biodiesel production with scientific and commercial interest. While yeast oils have been studied for decades, recent years showed literature suggesting optimization of cultivation processes as a good strategy to improve lipid accumulation in oleaginous yeasts. Some factors, like carbon nitrogen (C/N) ratio, pH and temperature are considered as the main parameters that affect the production of microbial oil. This study aimed to improve the optimization of cultivation process through multivariate analysis, increase the lipid accumulation and transform the condition non-oleaginous to an oleaginous condition in Candida zeylanoides QU 33. For optimization, response surface methodology was applied, using the levels of the variables temperature (20-35ºC), pH (2.0 to 6.0), and glucose concentration (10-40 g/100mL). The results showed two adjusted models to improve biomass production and lipid yield in C. zeylanoides QU 33, where temperatures lower than 28ºC, and glucose concentrations greater than 25% are favorable for the accumulation of lipids. The concentrations of glucose lower than 15% were unfavorable. The best conditions were observed in the experiment at 27.5 °C, pH 6 and 25% (w/v) glucose, with a lipid yield of 0.2 g/L and lipid content of 12.42% (g/g). Besides the unsatisfactory effort to change the oleaginicity condition for C. zeylanoides QU 33, our results confirmed the hypothesis to use this strain as a model of lipid metabolism for non-oleaginous yeast.

Our biology affects how we interact with the world, including how we learn new knowledge and respond to challenges. This article explores the impact of neurochemicals in our brain on learning and explains how to leverage our biology to improve education and problem-solving, focusing on computing education. Within this context, the article particularly examines the role of failure while learning. Learning, especially in technical fields, includes making errors on the path to success. While these errors trigger the necessary neurochemical conditions for rapid learning, these failures can also be demotivating. To gain the benefits of failure while mitigating its negative consequences, this article recommends evidence-based behavioral strategies for making the best out of failing while learning and designing for failure in learning environments.

Parkinson’s disease (PD) is a neurodegenerative disorder affecting motor and non-motor function. In advanced-stage PD, Deep Brain Stimulation (DBS) is applied to relieve motor symptoms. The subthalamic nucleus (STN) is a common target area. While STN-DBS efficiently relieves PD motor symptoms, there have been reports of adverse side-effects. This study collated reports on adverse symptoms reported as side-effects upon STN-DBS and examined their sex distribution; biological male vs biological female. Overall, most studies had a disproportion of males to females, or did not report sex at all. 17 original articles providing information of reporting patients´ sex, were identified and analysed. Articles included single case reports and larger clinical cohorts to cover the range STN-DBS related symptoms. Adverse side-effects were separated into three main categories (physiological, affective, cognitive). Sex-difference was detected in the all categories. Findings support recent publications of gender-differeces. Depression was the main affective side-effect reported by females. Males, but not females, reported aggressive behavior and sexual dysfunction. This review of published clinical reports revealed sex-differences in adverse side-effects of STN-DBS described by PD patients and also detected sex-differences in the descriptive details of reported side-effects. These findings advocate for sex-inclusion and streamlining of data to equalize information from both sexes when reporting beneficial and adverse effects of this treatment. With lack of equal level of description across sexes, progress in treatment is hampered. Further knowledge of sex-differences will support tailor-designed treatments for improved efficacy in PD patients of both sexes.

Our actions shape our everyday experience: what we experience, how we perceive and remember it, is deeply affected by how we interact with the world. Performing an action to deliver a stimulus engages neurophysiological processes which are reflected in the modulation of sensory and pupil responses. In this study, we hypothesized that these processes shape memory encoding, parsing the experience by grouping self- and externally-generated stimuli into differentiated events. Participants encoded sound sequences, in which either the first or last few sounds were self-generated and the rest externally-generated. We tested recall of the sequential order of sounds that had originated from the same (within event) or different sources (across events). Memory performance was not higher for within event sounds, suggesting that the memory representation was not structured by actions. However, during encoding, we replicated the well-known electrophysiological response attenuation, together with increased pupil dilation for self-generated sounds. Moreover, we found that at the boundary between events, physiological responses to the first sound originating from the new source were determined by the direction of the source switch. The results suggest that introducing actions, acts as a stronger contextual shift than removing them, despite not directly contributing to memory performance. The findings contribute to our understanding of how interacting with sensory input shapes our experiences, by addressing the unexplored relationships between action effects on sensory responses, pupil dilation and memory encoding, and discarding a meaningful contribution of low-level neurophysiological mechanisms associated to action execution in the modulation of memory.

Objective: To explore the characteristic lipid signature in placentas collected from normal pregnancies and those with mild and severe intrahepatic cholestasis of pregnancy (ICP). This research aims to clarify the pathogenesis and identify lipid biomarker for ICP through LC-MS/MS based lipidomic analysis. Design: Cross-sectional study, including normal pregnancy women and women with mild and severe ICP. Setting: Chengdu Women’s and Children’s Center Hospital. Population: Placenta samples collected from 30 normal pregnancy women and 30 mild and severe ICP women respectively. Women with normal pregnancy and ICP were recruit from April 2021 to July 2022 in Chengdu, China. Main outcome measures: Differentially expressed lipids. Results: Fourty-four lipids were differentially expressed both in mild and severe ICP placenta. The pathway analysis revealed these lipids are mainly enriched in glycerophospholipid metabolism and autophagy pathway. Weighted correlation network analysis (WGCNA) identified the correlation network module of lipids highly related to ICP. Using multiple logistic regression analysis, we identified three and four combined metabolites that had an area under receiver operating characteristic curves (AUC) ≥ 0.90. Conclusion: Our results systematically revealed the lipid signature in mild and severe ICP placenta. The results may provide new insight into the treatment and early prediction of ICP.

The aim of the study was to describe the use of two Janus kinase inhibitors (JAKi) in patients with rheumatoid arthritis (RA) recorded at Vall d’Hebron University Hospital (VHUH) between September 2017-November 2019. The data was extracted from the register of Patients and Treatments and medical records. Out of the thirty-nine patients included 90.0% were women, median age 56 years (IQR 29-72) and disease duration 15 years (IQR 8-20). Tofacitinib was the most used (28 patients). All patients had been previously treated with conventional synthetic disease modifying antirheumatic drugs (csDMARDs), and 79.5% with at least one biological DMARD (bDMARD). In 16 patients (41.0%) disease activity category improved. Treatment was withdrawn in 9 patients, mainly due to lack of efficacy (5). Five patients treated with tofacitinib reported gastrointestinal and/or skin disorders. This study shows the usefulness of registries of treated patients to study the use of the new and expensive drugs.

Background: Neural Networks, inspired by the human brain, are a class of machine learning models composed of interconnected artificial neurons. They have a rich history dating back to the 1940s, with notable advancements in the 1980s and 1990s when techniques like backpropagation enabled the training of multi-layer networks. Neural Networks have since experienced a renaissance, achieving state-of-the-art results in diverse domains. However, training them effectively remains a challenge. This thesis introduces Nano-AutoGrad, a system built upon automatic differentiation and optimization methods. Nano-AutoGrad efficiently computes gradients, facilitates parameter optimization, and incorporates mechanisms such as Multi-Perceptrons and Linear models. It also allows for expanding the network architecture with additional layers, enhancing the performance and representation capabilities of Neural Networks. The objective is to design and develop Nano-AutoGrad as an advanced tool for training complex models, leveraging historical advancements and computational graph understanding. Aim: This research is to design and develop Nano-AutoGrad as a simple easy compute Micro-Framework for training Neural Networks. By incorporating mechanisms such as Multi-Perceptrons, Linear models, and additional layers, the goal is to enhance network performance and versatility. The study of Nano-AutoGrad in training Linear models and achieving promising results. Nano-AutoGrad’s efficient gradient computation and parameter optimization contribute to advancements in Neural Network training Method: Nano-AutoGrad is a Micro-Framework that efficiently trains Neural Networks using automatic differentiation and optimization techniques and Computational graphs. It computes gradients by applying the chain rule, enabling parameter updates for improved performance. Optimization methods like Stochastic Gradient Descent (SGD), are utilized to iteratively update network parameters based on computed gradients. Conclusion: In our research, Through the development and evaluation of Nano-AutoGrad, this study highlights the effectiveness of historical advancements in Neural Networks, combined with automatic differentiation and optimization techniques. By incorporating mechanisms such as Multi-Perceptrons and Linear models, and expanding the network architecture with additional layers, Nano-AutoGrad demonstrates simple computational yet improved representation capabilities. The study showcases the potential of Nano-AutoGrad to contribute to the field of machine learning by providing a high-abstract tool for training Linear models and optimizing Neural Networks, building upon the rich history and progress in the field of Artificial intelligence.

Global software development (GSD) offers quality results, cost-effectiveness, and uninterrupted project delivery. However, integrating security into GSD remains a challenge. This study aims to enhance security in GSD projects by developing a hybrid approach using an empirical survey and Interpretive Structural Model (ISM). Initially, we identified 13 major security-coding risks and 82 practices to mitigate these by conducting a systematic literature review and questionnaire survey with 50 GSD security experts. Moreover, 13 experts were invited to analyze the interrelationships among the practices using ISM. The ISM analysis revealed that out of the identified security-coding practices, “never submit security measures to illegitimate authority”, “avoiding buffer overflow and format string vulnerabilities”, “control the brute force attack”, and “identify a middleman attack” were considered fully dependent. While “avoid revealing information to achieve a secure design” is entirely independent within the GSD security context. The study aids GSD professionals in assessing readiness in establishing contractual trust, understanding the current process pros and cons, and addressing urgent issues in secure software development processes.

The presence of insoluble components greatly minimizes the potential application of pulse proteins in beverage emulsions. Therefore, pea protein concentrate was mildly fractionated by aqueous centrifugation at 4,000g for 1 min to recover a soluble fraction (71% protein yield), which was then used to develop 5% oil-in-water emulsions using a high-pressure homogenizer. Emulsion stability was tested by heat treatment (90°C, 30 min) in the presence of NaCl (0-1M) at pH 7.0 and 2.0. Stability increased upon adding salt at pH 7, while at pH 2, proteins and droplets aggregated. Heat treatment led to extensive aggregation at both pH values due to denaturation and aggregation of proteins at the oil droplet surface, which was further worsened by salt. To prevent thermal destabilization, the proteins were heat-treated at 75°C for 30 min for partial denaturation before emulsification under hot conditions. The heat-treated protein-stabilized emulsions at pH 7 had superior thermal stability at all salt concentrations without aggregation. However, a similar improvement in stability was not observed at pH 2. Pre-heating the soluble protein exposed the hydrophobic patches, leading to better adsorption on the droplet surface, which did not show additional aggregation upon further heating the emulsions at pH 7. The heat-treated protein-stabilized emulsions showed about a 44% drop in lipid digestibility compared to the original emulsions. The proposed approach could be a valuable addition to the utilization of pea proteins in developing beverage emulsions that could withstand the heat treatment used during food processing.