Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study

Background and Objectives: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. Methods and Study Design: We conducted a study of adult septic patients in Intensive Care Unit (ICU), collecting 47 indicators for 14 days. After data cleaning and preprocessing, we used stats to explore energy delivery in deceased and surviving patients. We filtered out nutrition-related features and divided the data into three metabolic phases: acute early, acute late, and rehabilitation. Models were built using data before September 2020 and validated on the rest. We then established optimal energy target models for each phase using deep learning. Results: A total of 277 patients and 3115 data were included in this study. The models indicated that the optimal energy targets in the three phases were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy intake increased mortality rapidly in the early period of the acute phase. Insufficient energy in the late period of the acute phase significantly raised the mortality of septic patients. For the rehabilitation phase, too much or too little energy delivery both associated with high mortality. Conclusion: Our study established time-series prediction models for septic patients to optimize energy delivery in the ICU. This approach indicated the feasibility of developing nutritional tools for critically ill patients. We recommended permissive underfeeding only in the early acute phase. Later, increased energy intake may improve survival and settle energy debts caused by underfeeding

Whole exome sequencing of insulinoma reveals recurrent T372R mutations in YY1

Functional pancreatic neuroendocrine tumours (PNETs) are mainly represented by insulinoma, which secrete insulin independent of glucose and cause hypoglycaemia. The major genetic alterations in sporadic insulinomas are still unknown. Here we identify recurrent somatic T372R mutations in YY1 by whole exome sequencing of 10 sporadic insulinomas. Further screening in 103 additional insulinomas reveals this hotspot mutation in 30% (34/113) of all tumours. T372R mutation alters the expression of YY1 target genes in insulinomas. Clinically, the T372R mutation is associated with the later onset of tumours. Genotyping of YY1, a target of mTOR inhibitors, may contribute to medical treatment of insulinomas. Our findings highlight the importance of YY1 in pancreatic β-cells and may provide therapeutic targets for PNETs

High coverage of targeted lipidomics revealed lipid changes in the follicular fluid of patients with insulin-resistant polycystic ovary syndrome and a positive correlation between plasmalogens and oocyte quality

BackgroundPolycystic ovary syndrome with insulin resistance (PCOS-IR) is the most common endocrine and metabolic disease in women of reproductive age, and low fertility in PCOS patients may be associated with oocyte quality; however, the molecular mechanism through which PCOS-IR affects oocyte quality remains unknown.MethodsA total of 22 women with PCOS-IR and 23 women without polycystic ovary syndrome (control) who underwent in vitro fertilization and embryo transfer were recruited, and clinical information pertaining to oocyte quality was analyzed. Lipid components of follicular fluid (FF) were detected using high-coverage targeted lipidomics, which identified 344 lipid species belonging to 19 lipid classes. The exact lipid species associated with oocyte quality were identified.ResultsThe number (rate) of two pronuclear (2PN) zygotes, the number (rate) of 2PN cleaved embryos, and the number of high-quality embryos were significantly lower in the PCOS-IR group. A total of 19 individual lipid classes and 344 lipid species were identified and quantified. The concentrations of the 19 lipid species in the normal follicular fluid (control) ranged between 10-3 mol/L and 10-9 mol/L. In addition, 39 lipid species were significantly reduced in the PCOS-IR group, among which plasmalogens were positively correlated with oocyte quality.ConclusionsThis study measured the levels of various lipids in follicular fluid, identified a significantly altered lipid profile in the FF of PCOS-IR patients, and established a correlation between poor oocyte quality and plasmalogens in PCOS-IR patients. These findings have contributed to the development of plasmalogen replacement therapy to enhance oocyte quality and have improved culture medium formulations for oocyte in vitro maturation (IVM)

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Rapid Turnover of 2-LTR HIV-1 DNA during Early Stage of Highly Active Antiretroviral Therapy

BACKGROUND: Despite prolonged treatment with highly active antiretroviral therapy (HAART), the infectious HIV-1 continues to replicate and resides latently in the resting memory CD4+ T lymphocytes, which blocks the eradication of HIV-1. The viral persistence of HIV-1 is mainly caused by its proviral DNA being either linear nonintegrated, circular nonintegrated, or integrated. Previous reports have largely focused on the dynamics of HIV-1 DNA from the samples collected with relatively long time intervals during the process of disease and HAART treatment, which may have missed the intricate changes during the intervals in early treatment. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we investigated the dynamics of HIV-1 DNA in patients during the early phase of HARRT treatment. Using optimized real time PCR, we observed significant changes in 2-LTR during the first 12-week of treatment, while total and integrated HIV-1 DNA remained stable. The doubling time and half-life of 2-LTR were not correlated with the baseline and the rate of changes in plasma viral load and various CD4+ T-cell populations. Longitudinal analyses on 2-LTR sequences and plasma lipopolysaccharide (LPS) levels did not reveal any significant changes in the same treatment period. CONCLUSIONS/SIGNIFICANCE: Our study revealed the rapid changes in 2-LTR concentration in a relatively large number of patients during the early HAART treatment. The rapid changes indicate the rapid infusion and clearance of cells bearing 2-LTR in the peripheral blood. Those changes are not expected to be caused by the blocking of viral integration, as our study did not include the integrase inhibitor raltegravir. Our study helps better understand the dynamics of HIV-DNA and its potential role as a biomarker for the diseases and for the treatment efficacy of HAART

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation

This paper systematically examines the feasibility of using ultrasonic vibration excitation for rock breakage and fragmentation; it focuses on the failure mechanisms of rock mass under the impact of ultrasonic waves, and the development of ultrasonic technology. Laboratory testing using a self-designed system was conducted in this paper to further validate the efficiency and reliability of rock breakage using ultrasonics. The results show that: (i) under the effects of both the high speed impact of ultrasonic vibration excitation and induced rock vibration excitation, a fracture is initiated and propagates rapidly within and outside of the rock. Under ultrasonic vibration excitation for 140 s, the compressive strength decreased by 45.6%; (ii) under the excitation of ultrasonics, the rock specimens failed completely in a short time from inside to outside, and there are distinct fissures in the internal nucleation of the rock. It is suggested that ultrasonic excitation provides a novel and promising option for rock fragmentation and breakage, which optimises the efficiency of underground hard rock engineering

Biological Intelligence Inspired Trajectory Design for Energy Harvesting UAV Networks

In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. For this purpose, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the reward of serving users, the energy management, and the user service time. To capture the relationship among these factors, we first model the completion of service and the harvested energy as the reward, and the energy consumption during the deployment as the cost. Then, the deployment profitability is defined as the ratio of the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging-based algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability and minimize the average user service time. The proposed algorithm can find the optimal trajectory for the UAV with low time complexity at the level of polynomial. Fundamental analysis shows that the proposed algorithm achieves the maximal deployment profitability. Simulation results show that, compared to Q-learning algorithm, the proposed algorithm effectively reduces the operation time and the average user service time while achieving the maximal deployment profitability

Virtual inertia analysis of photovoltaic energy storage systems based on reduced-order model

The problem of non-ideal inertia of the photovoltaic energy storage system (PVESS) may occur due to unreasonable voltage control parameters. In response to this issue, this paper establishes an equivalent reduced-order model (EROM) for PVESS. This EROM considers the current control loop, voltage control loop and the virtual inertia control loop based on low-pass filter. This low-pass filter can effectively enhance the system’s virtual inertia. Since the output impedance of this EROM can visually reflect the external characteristics of the virtual inertia control loop, it is suitable for inertia analysis of PVESS. Furthermore, the impact of voltage control parameters and low-pass filter bandwidth on the system’s inertia is discussed from the perspective of the frequency response of the output impedance. Finally, the switch model of the PVESS is built on the RT-BOX hardware-in-the-loop experimental platform. The validity of the EROM and theoretical analysis is verified by several sets of experimental results

Coupling fermentation of glutamic acid and γ-polyglutamic acid and preparation of poly(amino acid) superabsorbent polymers

Abstract γ-polyglutamic acid (γ-PGA) is a biomarker that can be directly obtained by microbial fermentation. Poly(amino acid) superabsorbent polymers (SAPs) were prepared with purified γ-PGA as raw material and ethylene glycol diglycidyl ether (EGDGE) as a cross-linking agent. However, γ-PGA fermentation broth has a high viscosity, requires complex extraction and separation processes, and entails high energy consumption, resulting in the high cost of poly (amino acid) SAPs. Therefore, the coupling fermentation processes of glutamate polyglutamic acid, the process of using glutamate fermentation broth instead of pure glutamate powder for fermentation, and the process of treating the fermentation broth under conditions of centrifugation, UV irradiation, and high temperature, were studied. The results showed that the yield of γ-PGA after centrifugation decreased by 5%, but it did not affect the synthesis of hydrogels, and the addition of γ-PGA fermentation broth had a significant effect on the performance of γ-PGA-co-PASP SAPs. The proposed method not only helps avoid the separation of complex γ-PGA fermentation broth and reduces the cost, but it also helps improve the performance of the super-absorbent resin, which has great application potential