Number Of Conditions Research Articles

(011) PREVALENCE OF SEXUAL DYSFUNCTIONS AND HEALTHCARE NEEDS OF TRANSGENDER AND GENDER-DIVERSE PEOPLE: RESULTS FROM A REPRESENTATIVE CROSS-SECTIONAL SURVEY IN GERMANY

Abstract Introduction Sexual dysfunctions (SD) are a common issue for transgender-identified individuals and may persist despite access to gender-affirming care. Healthcare disparities experienced by transgender and gender-diverse people (TGDP) are often overlooked. Data on the prevalence of SD in TGDP from representative samples are rare, and little is known about the help-seeking behavior and healthcare needs regarding sexual problems in this subgroup compared to the needs of cisgender women (CW) and men (CM). Patient and Public Involvement can improve the quality and impact of research on under-researched topics. Objective This study aims to investigate the prevalence of SD in TGDP in the German population and to compare sexual health and healthcare needs between TGDP and the groups of CW and CM. Methods This cross-sectional study used data from an online survey with a sample representative of the German population in terms of gender, age, and federal state. ICD-11 criteria and FSDS-DAO were used to assess sexual health outcomes. Questionnaire items on gender, risk factors for SD, and healthcare needs were developed in collaboration with a culturally and gender-diverse group of eight external co-researchers and advisors following the patient and public involvement methodology. Outcomes were examined by gender group. Participants who reported identification with a gender different from their gender assigned at birth, a non-binary gender, a diagnosis related to gender incongruence, and/or variations of sex development were assigned to the TGDP group. Results Data from N=4,023 participants (CW: n=1,985, CM: n=1,815, TGDP: n=223) were collected with a mean age of 49.0 years (SD = 16.5). The 6-month prevalence of sexual dysfunction (SD) according to ICD-11 criteria was considerably higher among TGDP (34.4%) compared to CW (16.3%) and CM (16.6%), as reflected in the FSDS-DAO scores of the TGDP subgroup (M = 21.1, SD = 15.3), CW (M = 11.1, SD = 12.4), and CM (M = 12.3, SD = 12.7). The demand for information regarding sexual problems among those experiencing SD was more frequently reported by TGDP (67.5%) than by CW (44.0%) and CM (47.3%), with an odds ratio of 4.3 for TGDP vs. cisgender. Across all gender groups, the most preferred source for information was online research, followed by peer networks (29.0%) and friends (26.5%) in the TGDP group, in contrast to healthcare providers and partners for CW and CM. The most desired dialogue partners among healthcare providers (HCP) were urologists and general practitioners in TGDP (26.4% and 23.9%) and CM (37.3% and 25.4%), and gynecologists (36.0%) and psychotherapists (11.5%) for CW. Among TGDP with SD, 49.9% had undergone therapy for sexual dysfunctions (in CW 10.2%, in CM 15.2%), with psychotherapy (36.0%) being the most frequently reported treatment. The TGDP group showed higher needs for information and help, identifying the lack of services considering individual needs related to culture, gender identity, physical illness, and mental health conditions as the primary barriers to care. Diversity sensitivity training for HCPs, drug developments, improved resources, and 'blended care' solutions that encompass both digital and physical procedures were identified as the most urgent developments to improve care for sexual dysfunctions. Conclusions The results indicate the alarming extent of the burden of SD for TGDP. Furthermore, the findings emphasize the importance of tailored interventions that acknowledge and address the diverse needs of TGDP. Disclosure No.

Renewable energy integration for sustainable cold storage: Designing a solar PV operated thermoelectric cool chamber

Thermoelectric coolers utilizing peltier effect offer a promising solution for cooling applications. This study elaborates the design and optimization of a TEC-operated cool chamber and evaluation of developed thermoelectric cool chamber. A number of elements need to be carefully considered during the design process, including heat load assessment, TEC module selection and effective heat dissipation techniques. Appropriate TEC modules are chosen based on estimated cooling load, temperature difference achieved, power consumption, maximum cooling capacity of each TEC and heatsink. The solar photovoltaic operated thermoelectric cool chamber comprises essential components such as SPV Panel, solar charger controller, battery, PWM controller, TEC module with heat sink, and insulated box (TCC unit). The design considerations involve estimating total power consumption and determining the number and ratings of solar panels, batteries, and power conditioning devices to ensure reliable electricity supply. The system operates on SPV-generated power, utilizing MPPT charge controllers to maximize power output. Pulsed width modulation (PWM) systems efficiently regulate power distribution to thermoelectric modules, optimizing performance. The thermoelectric cool chamber was developed of 40 L water capacity. It was observed that, the chamber and water temperature in developed thermoelectric cool chamber was reduced from initial temperature by 11.85°C and 11.56°C, respectively after 24 hours. The chamber and water temperature after 24 hours were 13.31°C and 13.49°C, respectively. The performance evaluation showed that the chamber’s cooling efficiency was influenced by factors such as cooling load and water volume. This comprehensive approach enables the development of an effective and sustainable thermoelectric cooling system suitable for various applications.

A parameter inversion method for the probability integral method based on robust ridge estimation

The probability integral method is one of the most widely used methods for predicting surface subsidence induced by underground mining in China. In its parameter calculation, the least square algorithm is commonly employed for fitting parameters. However, in the process of fitting parameters, the results are easily affected by ill-conditioned normal matrices and the interference of outliers, resulting in divergent problems. To solve these problems, the principle of robust ridge estimation was introduced in this paper, and a parameter calculation model for the probability integral method based on this principle (hereafter referred to as the established model) was established. Besides, a parameter calculation experiment with manual intervention was conducted in combination with engineering examples. The results demonstrate that the parameter calculation method based on robust ridge estimation can suppress the interference of outliers, overcome the problem of ill-conditioned matrix, and ensure the effectiveness and reliability of parameter estimation results. Compared with the conventional least squares method, the robust ridge estimation method demonstrates greater accuracy in predicting surface subsidence parameters, which validates its rationality and accuracy in underground mining engineering. The research findings provide technical support for obtaining similar parameters for surface subsidence in mining areas and hold significant engineering application value.

Perception of the City Outskirts Women on the Barriers to Healthy Eating: A Qualitative Study

Background: A crucial prerequisite for managing non-communicable diseases (NCDs) and their risk factors is community experiences and perceptions about the challenges of unhealthy eating. On the other hand, there is no literature on nutritional literacy in the city›s outskirts, which are the vulnerable parts of society. Thus, this study aimed to examine the barriers to healthy nutrition for resident women in low-income areas of Mashhad. Methods: This qualitative study used 28 women as a representative sample of the residents of marginalized areas of Mashhad in Iran. Data collection was conducted using interviews between 2021 and 2022. Data analysis was done using a content analysis approach (open and selective coding). Results: The most critical obstacles related to healthy eating were listed as follows: (a) low income, high expenses or expensive, (b) incorrect habits or lack of awareness, (c) food preferences, (d) lack of feeling of necessity and insufficient motivation, (e) lack of convenient access in the area, and (f) illness or physical condition. However, participants stated economic, social, and cultural issues as the main barriers to choosing healthy food among residents of low-income areas. Conclusion: This study identified the main barriers to a healthy diet in marginal areas. Overcoming these obstacles can be a strategy for local and national planning to deal with NCDs.

The Chicken or the Egg? Understanding the Temporal Relationship Between Severe Mental Illness and Neurological Conditions in a UK Primary Care Cohort

The Chicken or the Egg? Understanding the Temporal Relationship Between Severe Mental Illness and Neurological Conditions in a UK Primary Care Cohort

Deep Weighted Fusion Learning (DWFL)-based multi-sensor fusion model for accurate building occupancy detection

With the advancement of artificial intelligence, the dominance of deep learning (DL) models over ordinary machine learning (ML) algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition. With the growing demands for power savings, building energy loss reduction could benefit from DL techniques. For buildings/rooms with the varying number of occupants, heating, ventilation, and air conditioning (HVAC) systems are often found in operations without much necessity. To reduce the building’s energy loss, accurate occupancy detection/prediction (ODP) results could be used to control the proper operations of HVACs. However, ODP is a challenging issue due to multiple reasons, such as improper selection/deployment of sensors, inefficient learning algorithms for pattern recognition, varying room conditions, etc. To overcome the above challenges, we propose a DL-based framework, i.e., Deep Weighted Fusion Learning (DWFL), to detect and predict occupancy counts with optimal multi-sensor fusion structure. DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor. Such weight assignment considers different room conditions and the pros/cons of each type of sensor. To evaluate DWFL model in terms of occupancy prediction accuracy, we have set up an experimental testbed with low-cost cameras, carbon dioxide (CO2), and passive infrared (PIR) sensors. Among the recently proposed occupancy detection models, DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88% accuracy in occupancy count estimation (Xue et al., 2019). Another deep learning-based model MI-PIR achieved 91% accuracy on raw analog data from PIR sensors (Andrews et al., 2020). Our research outcome is 94%. Therefore, the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

Penerapan Rom (Range Of Motion) Dan Massage Punggung Untuk Mencegah Luka Tekan Pada Lansia Tirah Baring

Immobilization is an extrinsic variable that can cause problems related to skin integrity. The inability to move freely due to situations that interfere with or limit movement may be directly related to an internal problem such as a chronic illness or health condition. Pressure ulcers or also known as decubitus ulcers, are local injuries caused by pressure or a combination of friction and pressure on the skin and/or underlying tissue, usually over a bony prominence. This research method uses a descriptive case study. The research population consisted of 1 person, namely a patient with heart failure with a nursing diagnosis of Activity Intolerance. The evaluation results from providing back massage (efflurage) and range of motion (ROM) to the client showed a reduction in complaints of soreness while in bed. After being taught range of motion (ROM) the client can do it themselves every time the client wakes up. Apart from that, clients also say they feel fresher after doing efflurage massage. The conclusion from applying ROM to these patients is that there is an increase in the Braden scale score which shows that there is a reduced risk of decubitus or pressure sores in the elderly. Meanwhile, when giving effluerage back massage, results were obtained in the form of client validation which said that the client was fresher, and there was a change in blood pressure results to normal numbers.

Solving Least-Squares Problems via a Double-Optimal Algorithm and a Variant of the Karush–Kuhn–Tucker Equation for Over-Determined Systems

A double optimal solution (DOS) of a least-squares problem Ax=b,A∈Rq×n with q≠n is derived in an m+1-dimensional varying affine Krylov subspace (VAKS); two minimization techniques exactly determine the m+1 expansion coefficients of the solution x in the VAKS. The minimal-norm solution can be obtained automatically regardless of whether the linear system is consistent or inconsistent. A new double optimal algorithm (DOA) is created; it is sufficiently time saving by inverting an m×m positive definite matrix at each iteration step, where m≪min(n,q). The properties of the DOA are investigated and the estimation of residual error is provided. The residual norms are proven to be strictly decreasing in the iterations; hence, the DOA is absolutely convergent. Numerical tests reveal the efficiency of the DOA for solving least-squares problems. The DOA is applicable to least-squares problems regardless of whether q<n or q>n. The Moore–Penrose inverse matrix is also addressed by adopting the DOA; the accuracy and efficiency of the proposed method are proven. The m+1-dimensional VAKS is different from the traditional m-dimensional affine Krylov subspace used in the conjugate gradient (CG)-type iterative algorithms CGNR (or CGLS) and CGRE (or Craig method) for solving least-squares problems with q>n. We propose a variant of the Karush–Kuhn–Tucker equation, and then we apply the partial pivoting Gaussian elimination method to solve the variant, which is better than the original Karush–Kuhn–Tucker equation, the CGNR and the CGNE for solving over-determined linear systems. Our main contribution is developing a double-optimization-based iterative algorithm in a varying affine Krylov subspace for effectively and accurately solving least-squares problems, even for a dense and ill-conditioned matrix A with q≪n or q≫n.

Validation of the rapid and simple LC-MS/MS method for the quantification of pregabalin in plasma of acutely poisoned patients

AbstractPregabalin is a gabapentinoid approved for the treatment of general anxiety disorder, neuropathic pain and as adjunctive therapy for focal seizures in patients with epilepsy. In addition, there are a number of conditions for which pregabalin is prescribed off-label. Along with the widespread use there are a significant number of reports describing the misuse of pregabalin over the last decade. Over time, it became clear that pregabalin should become part of routine testing in toxicology laboratories. The aim of this paper was to present validation of a LC-MS/MS method for the quantification of pregabalin in plasma of acutely poisoned patients. Simple sample preparation step and rapid chromatographic separation shortened the overall analysis time, which was the goal of method development. The presence of pregabalin was confirmed with three ion transitions, ensuring high selectivity of the validated method. The statistical data obtained showed good precision and accuracy over a wide concentration range. No endogenous or other interference was detected, and there was no matrix effect influence with this method. The LC-MS/MS method was applied to quantify pregabalin in plasma samples of patients admitted to the emergency department due to a possible acute pregabalin overdose. Different concentrations were found, and we report, to the best of our knowledge, the highest plasma concentration of pregabalin in the plasma of a patient with acute poisoning. In conclusion, we developed a fast and simple LC-MS/MS method for reliable determination of pregabalin and demonstrated the developed method was suitable for routine use in clinical toxicology setting.

Conditional [formula omitted]-matching preclusion for [formula omitted]-dimensional torus networks

The k-matching preclusion number of a graph is the minimum number of edges whose deletion results in the remaining graph that has neither perfect k-matchings nor almost perfect k-matchings. For many networks, their optimal k-matching preclusion sets are precisely those edges incident with a single vertex. In this paper, we introduce the concept of conditional k-matching preclusion, in which isolated vertices are not permitted in fault networks. We establish the conditional k-matching preclusion numbers and all possible minimum conditional k-matching preclusion sets for n-dimensional torus networks with n≥3. In addition, we investigate the relationship between all optimal sets for three kinds of (conditional) matching preclusion problems.

Nonlinear coupled multi-physics field analysis of permanent magnet synchronous motor combining finite element analysis with optimized meshless method

Aiming at significant disadvantages of finite element analysis (FEA) performed in coupled multiphysics field calculation, including high computing cost and strong dependency on meshing quality. Taking a high-speed permanent magnet synchronous motor (P MSM) as an example, an approach combining FEA with optimized meshless method is proposed to solve the heat transfer problem. Firstly, electromagnetic losses under rated and 1.2 times overloaded operation conditions are calculated using FEA respectively. Secondly, three-dimensional FEA flow models considering the influence of viscosity varying with temperature are established. Thirdly, convective heat transfer coefficients are calculated as the boundary condition, after which the electromagnetic losses as heat sources are mapped to the threedimensional steady-state temperature field calculations using FEA and conventional meshless method respectively. Applying the above two methods, a singular boundary method (SBM) is employed to replace the temporal derivative term of the transient calculation equation. Finally, to mitigate the illconditioned matrix appearing in the conventional meshless method coupled with FEA, an optimized meshless method is proposed to recalculate the heat transfer equation. After that, the calculation results of the above methods are compared with experimental results. This research is beneficial for the multiphysical coupling analysis of electrical equipment with complex structures considering nonlinear factors.

Telehealth Utilization and Associations in the United States During the Third Year of the COVID-19 Pandemic: Population-Based Survey Study in 2022.

The COVID-19 pandemic rapidly changed the landscape of clinical practice in the United States; telehealth became an essential mode of health care delivery, yet many components of telehealth use remain unknown years after the disease's emergence. We aim to comprehensively assess telehealth use and its associated factors in the United States. This cross-sectional study used a nationally representative survey (Health Information National Trends Survey) administered to US adults (≥18 years) from March 2022 through November 2022. To assess telehealth adoption, perceptions of telehealth, satisfaction with telehealth, and the telehealth care purpose, we conducted weighted descriptive analyses. To identify the subpopulations with low adoption of telehealth, we developed a weighted multivariable logistic regression model. Among a total of 6252 survey participants, 39.3% (2517/6252) reported telehealth use in the past 12 months (video: 1110/6252, 17.8%; audio: 876/6252, 11.6%). The most prominent reason for not using telehealth was due to telehealth providers failing to offer this option (2200/3529, 63%). The most common reason for respondents not using offered telehealth services was a preference for in-person care (527/578, 84.4%). Primary motivations to use telehealth were providers' recommendations (1716/2517, 72.7%) and convenience (1516/2517, 65.6%), mainly for acute minor illness (600/2397, 29.7%) and chronic condition management (583/2397, 21.4%), yet care purposes differed by age, race/ethnicity, and income. The satisfaction rate was predominately high, with no technical problems (1829/2517, 80.5%), comparable care quality to that of in-person care (1779/2517, 75%), and no privacy concerns (1958/2517, 83.7%). Younger individuals (odd ratios [ORs] 1.48-2.23; 18-64 years vs ≥75 years), women (OR 1.33, 95% CI 1.09-1.61), Hispanic individuals (OR 1.37, 95% CI 1.05-1.80; vs non-Hispanic White), those with more education (OR 1.72, 95% CI 1.03-2.87; at least a college graduate vs less than high school), unemployed individuals (OR 1.25, 95% CI 1.02-1.54), insured individuals (OR 1.83, 95% CI 1.25-2.69), or those with poor general health status (OR 1.66, 95% CI 1.30-2.13) had higher odds of using telehealth. To our best knowledge, this is among the first studies to examine patient factors around telehealth use, including motivations to use, perceptions of, satisfaction with, and care purpose of telehealth, as well as sociodemographic factors associated with telehealth adoption using a nationally representative survey. The wide array of descriptive findings and identified associations will help providers and health systems understand the factors that drive patients toward or away from telehealth visits as the technology becomes more routinely available across the United States, providing future directions for telehealth use and telehealth research.

Reentry trajectory planning for hypersonic vehicles via an improved sequential convex programming method

The reentry trajectory planning problem of hypersonic vehicles is generally a continuous and nonconvex optimization problem. The solution efficiency and convergence properties of the planning algorithm become an essential point when considering the practical application scenarios. In this paper, an augmented-Lagrange-based improved sequential convex programming algorithm is proposed to solve it and achieve superior solutions with higher accuracy and efficiency. Firstly, the mapped Chebyshev pseudo-spectral method is utilized to convert the continuous optimal control problem into a highly approximately discretized one. In this framework, the discretization technique is employed to discretize the states and control variables at the time interval, which leads to the improvement of ill-conditioned differential matrix and better approximation properties of the variables. Moreover, by introducing slack variables, automatically updated penalty parameters and Lagrange multipliers during the convexification processing, the original nonconvex problem is transformed into an augmented-Lagrange-function-formed relaxed convex one. Then, the arrived convex subproblem can be solved iteratively by the matured interior-point method until the predetermined precision is satisfied. Based on this transformation, it can effectively alleviate the numerical difficulty and improve the convergence property of the proposed improved sequential convex programming algorithm. Finally, Rigorous theoretical analysis is conducted to prove that the solution sequence generated by the proposed algorithm could converge to an approximate global solution of the original problem. Numerical simulations are also presented, and it is demonstrated that the solution time of the proposed algorithm is more than 95% less than that of the traditional nonlinear programming methods without losing the merits of high precision.

Strain field reconstruction from helical-winding fiber distributed acoustic sensing and its application in anisotropic elastic reverse time migration

Optical fiber-based distributed acoustic sensing (DAS) technology has been a popular seismic acquisition tool due to its easy deployment, wide bandwidth, and dense sampling. However, the sensitivity of straight optical fiber to only single-axis strain presents challenges in fully characterizing multicomponent seismic wavefields, making it difficult to use these data in elastic reverse time migration (ERTM). The helical-winding fiber receives projecting signals projected onto the fiber from all seismic strain field components and has the potential to reconstruct those strain components for ERTM imaging. Here, we give detailed mathematical principles of helical fiber-based DAS with crucial parameters such as pitch angle, gauge length, and rotating angle. At least six points of DAS responses are required in one or several winding periods to rebuild the strain fields within the seismic wavelength. The projecting matrix of conventional regular helical-winding fiber is singular and ill conditioned, which results in computation challenges for the inverse of the Hessian matrix for strain component reconstruction. To tackle this problem, we develop a nonregular variant pitch-angle winding configuration for helical fiber. Our winding design is validated using the rank and condition number of the projecting matrix, which is proven to be an important tool in the reconstruction of the original seismic strains. The recovered strain components from the DAS response are then used to backward propagate the receiver wavefields in ERTM with an efficient P/S decoupled approach. To summarize, we develop a novel winding design of helical fiber to recover the strain fields and then develop an efficient 3D anisotropic P/S wave-mode decomposition method for generating vector P and S wavefields during their propagation. Both methods are applied to build an anisotropic DAS-ERTM workflow for producing PP and PS images. Two synthetic examples demonstrate the effectiveness of our approach.

Serial indirect effects from childhood maltreatment to adult chronic health conditions through contemporary family relationships and mental health problems: Inquiry into sleep disturbances and stress.

Childhood maltreatment accelerates biological aging, leaving adults vulnerable to chronic health problems. There is robust evidence that social relationships, including those with family members, may influence chronic health problems through psychological pathways, but there is little research considering stress and sleep problems, especially among adults who experienced childhood maltreatment. Furthermore, longitudinal research is lacking related to maltreatment and chronic health problems. The current study examined familial support and strain and subsequent sleep problems and stress in a serial mediational model linking childhood maltreatment to chronic health problems over time. Using three waves of data from the study of Midlife Development in the United States (N = 859; 55.8% female), structural equation modeling was used to examine familial support, strain, stress, and sleep problems in serial mediational model linking maltreatment to the number of chronic health conditions over a 9-year period. Childhood maltreatment was indirectly associated with a number of chronic health conditions through familial support and strain through subsequent reports of stress. Although family support was associated with fewer sleep problems, the bootstrapped indirect effect was not significant. Simple indirect effects from maltreatment to the number of chronic health problems were significant through both sleep problems and stress. Contemporary family relationships and psychological problems are possible points of prevention and intervention reducing the number of chronic health conditions among adults who were maltreated in childhood. Focusing on familial relationships and stress processes may be particularly fruitful. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Evaluation of late effects during a 21-year follow-up of pediatric Hodgkin lymphoma survivors: Experience of a pediatric cancer center in Turkey, as a developing country model.

Long-term survivors of Hodgkin lymphoma (HL) are at risk of developing a range of late effects, with a second malignant neoplasm and cardiovascular diseases being the leading causes of death in these patients. The present study aims to evaluate the late side effects in children with HL. Out of 53 HL patients, we assessed the long-term effects of childhood HL survivors (HLSs; n = 50) diagnosed between 1998 and 2019. Patient data related to chronic health conditions, and sociodemographic characteristics were compared with their siblings ( n = 56). The cumulative overall survival (OS) at 1, 5, and 10 years from diagnosis was 98.1 ± 1.9%, 93.3 ± 3.8%, and 93.3 ± 3.8%, respectively. Groups of HLSs and their siblings were matched according to age and gender. Compared with siblings, survivors had will be changed as 'a higher frequency of nephrotoxicity ( P = 0.02)', cardiotoxicity ( P = 0.12), thyroid dysfunction ( P = 0.001), health care service usage ( P < 0.01), limitation of physical function ( P = 0.01), and pulmonary disease ( P = 0.01). The control group of siblings had a higher incidence of marital status ( P < 0.01), parenthood ( P = 0.01), and smoking habit ( P = 0.03). Thyroid dysfunction was associated with neck radiotherapy ( P < 0.01). No secondary neoplasm was detected. In relapsed, refractory setting ( n = 10), autologous transplantation ( n = 9) is performed after a complete remission. Brentuximab vedotin with or without bendamustine and rituximab is also used in selected patients. Increased number of chronic health conditions and social problems point to the significance of long-term follow-up of HLSs. We are currently preparing a survivorship guideline appropriate for Turkey's conditions. Renal, heart, pulmonary impairment, thyroid dysfunction, limitation in physical functioning, and deterioration in social status (marriage, having children, education).

294 Strategies for engaging patients in building a model for patient partner engagement to accelerate translational science

OBJECTIVES/GOALS: To describe the strategies our newly created Patient Partners Program has planned for engaging diverse patients in the co-creation of our planned Patient Partner Academy. We will elicit feedback from other CTSAs about their strategies for promoting individual patient partnerships in research. METHODS/STUDY POPULATION: Our program to promote authentic patient-researcher partnerships to advance clinical and translational research is grounded in participatory approaches to maximize meaningful engagement. The process of creating our Patient Partners Academy is co-led by a patient partner and national leader in re-envisioning the role of patients in research, and a University of Michigan faculty member who advocates for patient partnerships in research. Listening sessions and community engagement studios will involve the developers of patient partner training programs, patient partners who have received research capacity training (as well as researchers and patient partners interested in collaborative research. Insights from these sessions will inform the development of learning models and curriculum content. RESULTS/ANTICIPATED RESULTS: Through individual listening sessions and community engagement studios we will collect data on the barriers and facilitators to patient partner engagement in research and the experiences and preferred learning models in patient partner training programs. Both the listening sessions and community engagement studios will be recorded, transcribed, and analyzed for common themes. We aim to answer three questions: What do patient partners need to be meaningfully engaged as equal partners across the research continuum? How can we best engage people who experience serious illness or chronic conditions and may be underserved by healthcare systems? What do researchers, faculty, and clinicians need to create trusting, collaborative relationships with patient partners? DISCUSSION/SIGNIFICANCE: Integrating patient partners into study teams accelerates innovation and translational science, increases the relevance of research findings, improves health outcomes and patient empowerment, and elevates the value of the patient perspective allowing researchers to gain a new point of view from an individual with lived experience.

Liraglutide-Induced Depression with Suicidality in an Obese Adult: A Case Report

Obesity is a major health issue worldwide. Treating adults with obesity often involves lifestyle and diet changes and sometimes medication. Liraglutide is a drug that is being closely studied for treating obesity. However, the potential side effects of liraglutide, particularly its impact on mood and the development of depression, may be of concern. Given the frequent co-occurrence of obesity and depression, it is important to understand how obesity treatments like liraglutide might affect a person’s mood. A 47-year-old schoolteacher with no personal or family history of mental illness or chronic physical condition sought help for his steadily increasing weight. Despite having a sedentary job and reporting no major stress or substance use, he did not attempt to change his diet or activity level, occasionally experiencing sleep difficulties. He was overweight, with a body mass index of 42. The patient was diagnosed with adult obesity and prescribed liraglutide, diet changes, and exercise. Initially, he lost weight, but he also developed depressive symptoms, including fatigue, loss of interest, sleep disturbances, and suicidal ideation. Following cessation of liraglutide treatment, his depression symptoms got better, but his weight slightly increased. This case sheds light on the possible link between liraglutide and depression in managing obesity. It is crucial for healthcare providers to be aware of potential mental health side effects of obesity drugs like liraglutide. Although the exact reasons behind these mood changes are not fully understood, this case emphasizes the need for careful observation and decision-making in treatment. Understanding these issues can help balance the benefits and risks of liraglutide and ensure better care and treatment options for people with obesity.

Analytical solutions for dispersions and waveforms of acoustic logging in a cased hole

Acoustic logging is one of the most promising methods for the quantitative evaluation of cement bond conditions in cased holes. However, inefficient use of full-wave information yields unsatisfactory interpretation accuracy. Fundamentally, this is because the wavefield characteristics have not been thoroughly investigated under various cement bonding conditions. Thus, this study derives analytical solutions of wavefields for a single-cased-hole model and emphasizes the dispersion calculation algorithm. To solve the dispersion equation when solving for the poles of the propagating modes with real wavenumbers, we renormalize the Bessel function related to the borehole fluid by multiplying it with an attenuation factor. For leaky modes with complex wavenumbers, we develop a novel method to find local peaks of the matrix condition number (LPMCN) in the frequency domain to determine dispersion poles, avoiding the local optimization issues resulting from the traditional Gauss-Newton iteration method. Combining these two methods, we establish a fast and accurate workflow for evaluating the dispersion of all modes in cased holes using a relatively fast bisection method to manage the dispersion of the propagating modes and using the LPMCN method to derive the dispersion curves of leaky modes. Furthermore, all propagating modes are individually investigated in the monopole measurement by evaluating the residues of the real poles in a casing-free model. The analysis demonstrates that the first-order pseudo-Rayleigh wave (PR1) and inner Stoneley wave (ST1) are the two strongest modes. Finally, we focus on the waveforms and dispersion characteristics of the outer Stoneley wave (ST2) related to the fluid channel in the cement annulus. The results reveal that as the fluid thickness increases, the phase velocity of the ST2 mode decreases, and its amplitude increases. Therefore, the ST2 mode can potentially evaluate the thickness of the fluid channel in a cement annulus if an effective weak-signal-extraction method is used.

Decentralized Sum-of-Nonconvex Optimization

We consider the optimization problem of minimizing the sum-of-nonconvex function, i.e., a convex function that is the average of nonconvex components. The existing stochastic algorithms for such a problem only focus on a single machine and the centralized scenario. In this paper, we study the sum-of-nonconvex optimization in the decentralized setting. We present a new theoretical analysis of the PMGT-SVRG algorithm for this problem and prove the linear convergence of their approach. However, the convergence rate of the PMGT-SVRG algorithm has a linear dependency on the condition number, which is undesirable for the ill-conditioned problem. To remedy this issue, we propose an accelerated stochastic decentralized first-order algorithm by incorporating the techniques of acceleration, gradient tracking, and multi-consensus mixing into the SVRG algorithm. The convergence rate of the proposed method has a square-root dependency on the condition number. The numerical experiments validate the theoretical guarantee of our proposed algorithms on both synthetic and real-world datasets.