Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Kinase Inhibitor Therapies for Fragile X Syndrome: Tweaking Twists in the Autism Spectrum Kinase Signaling Network

Absence of the Fragile X Messenger Ribonucleoprotein 1 (FMRP) causes autism spectrum disorders and intellectual disability, commonly referred to as the Fragile X syndrome. FMRP is a negative regulator of protein translation and is essential for neuronal development and synapse formation. FMRP is a target for several post-translational modifications (PTMs) such as phosphorylation and methylation, which tightly regulate its cellular functions. Studies have indicated the involvement of FMRP in a multitude of cellular pathways, and an absence of FMRP was shown to affect several neurotransmitter receptors, for example, the GABA receptor and intracellular signaling molecules such as Akt, ERK, mTOR, and GSK3. Interestingly, many of these molecules function as protein kinases or phosphatases and thus are potentially amendable by pharmacological treatment. Several treatments acting on these kinase-phosphatase systems have been shown to be successful in preclinical models; however, they have failed to convincingly show any improvements in clinical trials. In this review, we highlight the different protein kinase and phosphatase studies that have been performed in the Fragile X syndrome. In our opinion, some of the paradoxical study conclusions are potentially due to the lack of insight into integrative kinase signaling networks in the disease. Quantitative proteome analyses have been performed in several models for the FXS to determine global molecular processes in FXS. However, only one phosphoproteomics study has been carried out in Fmr1 knock-out mouse embryonic fibroblasts, and it showed dysfunctional protein kinase and phosphatase signaling hubs in the brain. This suggests that the further use of phosphoproteomics approaches in Fragile X syndrome holds promise for identifying novel targets for kinase inhibitor therapies.
Effects of Butylphthalide Sodium Chloride Injection Combined with Edaravone Dexborneol on Neurological Function and Serum Inflammatory Factor Levels in Sufferers Having Acute Ischemic Stroke

For investigating an influence on butylphthalide sodium chloride injection combined with edaravone dexborneol on neurological function and serum inflammatory factor levels in sufferers having acute ischemic stroke, 120 sufferers having acute ischemic stroke from September 2020 to September 2021 are chosen for the study subjects. In line with the diverse therapies, they took part in a control group and the study group, with 60 examples in each group. The control group is treated with edaravone dexborneol, and the study group is treated with butylphthalide sodium chloride injection, based on the control group. The posttreatment curative efficacy on the two groups is recorded, and treatment of both the two groups is compared. Before and after neurological function indexes (NIHSS and mRS), inflammatory factor indexes (IL-6, CRP, and TNF-α), life quality index (Barthel index), hemorheological indexes (plasma-specific viscosity), and neurological levels of NSE are logged and contrasted between the two groups of adverse reactions during therapy. Postcure, the overall response rate and Barthel index of the study group obviously overtop those of the control group (p < 0.05). IL-6, CRP, TNF-α, NSE, plasma specific viscosity, and NIHSS and mRS scores obviously hypodown those of the control group (p < 0.05), and untoward effects on the two groups during curing are lower, and the discrepancy is not obvious(p > 0.05). Butylphthalide sodium chloride injection combined with edaravone dexborneol can enhance curative efficacy on sufferers having acute ischemic stroke, improve neurological function, blood rheology, and quality of life, and decrease the secretion of cytokine, having a better effect and high medication safety.
Clavulanic Acid: A Novel Potential Agent in Prevention and Treatment of Scopolamine-Induced Alzheimer’s Disease

Background and Aim: Alzheimer’s disease (AD) is the most common form of dementia in the elderly. It is characterized as a multifaced disorder with a greater genetic contribution. The contribution of many genes such as BDNF, Sirtuin 6, and Seladin 1 has been reported in the pathogenesis of AD. Current therapies include acetylcholinesterase inhibitors and N-methyl-d-aspartate receptor antagonists, which are only temporarily beneficial. Therefore, it seems that more studies should be conducted to determine the exact mechanisms of drugs to deal with the diseases’ multifactorial features that we face. Methods: In this study, 42 adult rats were randomly divided into 7 groups and received drugs intraperitoneally and orally according to the protocol as follows: scopolamine group, clavulanic acid group, memantine group, scopolamine + memantine group, clavulanic acid pre- and post-treatment, and normal saline group. The Morris water maze method was performed to evaluate the spatial memory of animals, and the terminal deoxynucleotidyl transferase dUTP nick end labeling assay and real-time polymerase chain reaction were performed to study neuronal cell apoptosis and gene expression, respectively. Results: Significant differences were observed in the spatial memory of rats that received clavulanic acid prophylactically compared to the Alzheimer’s model on the day of the test. Moreover, the results obtained during the training showed that both memantine and clavulanic acid improved spatial memory by increasing the time of rats present in the platform position and by reducing the swimming time in the scopolamine-induced Alzheimer’s group. Besides, rats that received clavulanic acid and memantine had a greater percentage of healthy cells in comparison with the scopolamine-induced Alzheimer’s group; however, the results were more significant for clavulanic acid. Furthermore, the expressions of BDNF, Seladin 1, and Sirtuin 6 as neuroprotective target genes were modified after clavulanic acid and memantine administrations; similarly, the results obtained from clavulanic acid were more significant. Conclusion: The results show that the administration of clavulanic acid before and after the use of scopolamine can reduce the percentage of apoptotic cells in the hippocampus and also improve the parameters related to learning and spatial memory; however, its effect in the prophylactic state was stronger. The results obtained from memantine revealed that it has neuroprotective potency against AD; however, clavulanic acid had a greater effect. Also, with increased expression of the neuroprotective genes, clavulanic acid could be considered as an option in the upcoming preclinical and clinical research about Alzheimer’s disease.
Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning (RL), more specifically, the Q-learning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users (PUs), and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users (SUs), and they could be un-licensed subscribers that don't pay for their service, device-to-device communications, or sensors. Each user whether it’s a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost 90% utilization of the spectrum, and provided fair shares of the spectrum among users.
A Disaster Situation Management Study using MORT-TAC

Purpose: The purpose of this study is to present a plan to manage disaster situations using MORT-TAC, a factor to consider the situation evaluation of disaster sites. Despite the importance of disaster response due to the sharing of initial disaster information in disaster response, small disasters often develop into national disasters due to the lack of important information in the event of a disaster. Method: In order to conduct research using MORT-TAC, a factor in evaluating disaster situations, various organizations can use MORT-TAC to distribute initial disaster information through literature research and tele-phone interviews due to COVID-19. In addition, a study was conducted to incorporate the researcher's military expertise for 30 years of military service into the disaster field. Results: As a result of the district, MORT-TAC was suggested to distribute initial information on disaster sites between city and county emergency control teams, integrated support headquarters, regional and central emergency control teams, and education and training programs to improve disaster-related officials' expertise. Conclusion: Disaster response and disaster situation management must be performed by professional officials. Disaster officials of all disaster response emergency organizations must have the expertise to manage disaster situations using MORT-TAC, and to this end, education and training programs must be reflected in the annual education plan. In addition, in-depth research on MORT-TAC should be continuously studied in the future.
Treatment of Transthyretin Amyloid Cardiomyopathy: The Current Options, the Future, and the Challenges

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressively debilitating, rare disease associated with high mortality. ATTR-CM occurs when TTR amyloid protein builds up in the myocardium along with different organs, most commonly the peripheral and the autonomic nervous systems. Managing the cardiac complications with standard heart failure medications is difficult due to the challenge to maintain a balance between the high filling pressure associated with restricted ventricular volume and the low cardiac output. To date, tafamidis is the only agent approved for ATTR-CM treatment. Besides, several agents, including green tea, tolcapone, and diflunisal, are used off-label in ATTR-CM patients. Novel therapies using RNA interference also offer clinical promise. Patisiran and inotersen are currently approved for ATTR-polyneuropathy of hereditary origin and are under investigation for ATTR-CM. Monoclonal antibodies in the early development phases carry hope for amyloid deposit clearance. Despite several drug candidates in the clinical development pipeline, the small ATTR-CM patient population raises several challenges. This review describes current and future therapies for ATTR-CM and sheds light on the clinical development hurdles facing them.
Effect of Perceived Social Support, Spiritual Well-being, Health Literacy, and Resilience on Quality of Life in Patients Undergoing Hemodialysis: A Structural Equation ModelModel

Background: Spiritual well-being, social support, health literacy, and resilience may affect the quality of life (QOL) in patients undergoing hemodialysis. Objectives: The purpose of this study was to investigate the effect of perceived social support, spiritual well-being, health literacy, and resilience on the QOL in patients under hemodialysis. Methods: This descriptive cross-sectioanl study randomly included 260 patients from three educational hospitals in Ahvaz, Iran. Data were collected using a 36-item QOL questionnaire, Spiritual Health Questionnaire (SHQ), Perceived Social Support (PSS) Questionnaire, Resilience Questionnaire, and Montazeri et al.’s Health Literacy for Iranian Adults (HELIA) Questionnaire. Stata software version 14 was used for data analysis. The results were evaluated using descriptive statistics and a structural equation model (SEM). Results: In this study, a total of 260 patients (male: 138 vs. female: 122; mean age: 51.73 ± 15.32 years) undergoing hemodialysis were included. Most of the patients were married (76.9%) and had at least one comorbid disease (59.2%). Findings showed that health literacy, spiritual well-being, and social support had a significant correlation with the QOL (P < 0.001). However, resilience was not significantly related to the patients’ QOL (P > 0.58). Conclusions: To improve the QOL of patients undergoing hemodialysis, health care providers need to improve patients’ health literacy, social relationships, and spiritual well-being.
Multi-Night at-Home Evaluation of Improved Sleep Detection and Classification with a Memory-Enhanced Consumer Sleep Tracker

Purpose To evaluate the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware. Patients and Methods 58 healthy, East Asian adults aged 23–69 years (M = 37.10, SD = 13.03, 32 males), each underwent 3 nights of PSG at home, wearing 2nd Generation Oura Rings equipped with additional memory to store raw data from accelerometer, infra-red photoplethysmography and temperature sensors. 2-stage and 4-stage sleep classifications using a new machine-learning algorithm (Gen3) trained on a diverse and independent dataset were compared to the existing consumer algorithm (Gen2) for whole-night and epoch-by-epoch metrics. Results Gen 3 outperformed its predecessor with a mean (SD) accuracy of 92.6% (0.04), sensitivity of 94.9% (0.03), and specificity of 78.5% (0.11); corresponding to a 3%, 2.8% and 6.2% improvement from Gen2 across the three nights, with Cohen’s d values >0.39, t values >2.69, and p values <0.01. Notably, Gen 3 showed robust performance comparable to PSG in its assessment of sleep latency, light sleep, rapid eye movement (REM), and wake after sleep onset (WASO) duration. Participants <40 years of age benefited more from the upgrade with less measurement bias for total sleep time (TST), WASO, light sleep and sleep efficiency compared to those ≥40 years. Males showed greater improvements on TST and REM sleep measurement bias compared to females, while females benefitted more for deep sleep measures compared to males. Conclusion These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.
Analysis of complexity and modulation spectra parameterizations to characterize voice roughness

Disordered voices are frequently assessed by speech pathologists using acoustic perceptual evaluations. This might lead to problems due to the subjective nature of the process and due to the in uence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations the design of new indicator parameters obtained from voice signal processing is desirable. With that in mind, this paper presents an automatic evaluation system which emulates perceptual assessments of the roughness level in human voice. Two parameterization methods are used: complexity, which has already been used successfully in previous works, and modulation spectra. For the latter, a new group of parameters has been proposed as Low Modulation Ratio (LMR), Contrast (MSW) and Homogeneity (MSH). The tested methodology also employs PCA and LDA to reduce the dimensionality of the feature space, and GMM classiffers for evaluating the ability of the proposed features on distinguishing the different roughness levels. An effciency of 82% and a Cohen's Kappa Index of 0:73 is obtained using the modulation spectra parameters, while the complexity parameters performed 73% and 0:58 respectively. The obtained results indicate the usefulness of the proposed modulation spectra features for the automatic evaluation of voice roughness which can derive in new parameters to be useful for clinicians.
Atendiendo compromisos globales y necesidades hídricas locales: priorización de áreas de restauración en Costa Rica con la Herramienta de Optimización de Oportunidades de Restauración

Este informe presenta una metodología espacialmente explícita para identificar áreas prioritarias para la restauración funcional del paisaje a partir de la optimización de la prestación de servicios ecosistémicos. Este estudio profundiza también en el análisis realizado con la Metodología de Evaluación de Oportunidades de Restauración (ROAM) en Costa Rica. El objetivo de esta publicación es mostrar cómo el uso de la Herramienta de Optimización de Oportunidades de Restauración (ROOT, por sus siglas en inglés) puede informar cuales áreas generarán el mayor impacto positivo con las acciones de restauración propuestas por el Comité Técnico de Restauración de Costa Rica. El análisis muestra el impacto de las acciones propuestas sobre los servicios ecosistémicos y su demanda para aportar al diseño de proyectos, y dar a conocer programas o estrategias para la restauración de paisajes.
Economic Mechanisms for Integrating Smart Grid Technologies into Power Systems

The electric power system is undergoing a fundamental transition towards a sustainable and intelligent system, commonly known as the smart grid. To enable such a transition, various smart grid technologies have been developed, including demand response, clean energy technologies, energy storage and decentralized operation of power systems in the form of microgrids. These smart grid technologies bring both opportunities and challenges to the shortterm operation and long-term investment of power system. This thesis aims to evaluate the economic impact of smart grid technologies and design economic mechanisms to integrate them into power systems. In particular, this thesis studies pricing scheme design for the integration of demand response, investment strategy for the integration of renewable energy and energy storage, and cooperative operation and planning of interconnected microgrids. To integrate demand response of data centers, we design a dynamic pricing scheme, which explores the flexibility of data centers’ energy consumption for electric load balancing across both geographical locations and time periods. We model the interactions between a utility company and data centers as a two-stage optimization problem, and design two algorithms to compute the optimal solution and close-to-optimal solution with low computational complexity, respectively. We show that the optimal dynamic pricing leads to a win-win solution, such that data centers reduce operational cost and utility
Stimulate creative ideas generation for eco-innovation: an experimentation to compare eco-design and creativity tools

In the literature there are many propositions of eco-design tools on the one hand and creativity tools on the other hand to support the early creative design phases of products and services development. In this field, one of the challenges of the eco-innovation approach consists in hybridizing these two domains to promote the generation of creative concepts in a more sustainable perspective. Consequently, eco-innovation implies to develop new support methods and tools that meet specific requirements to encourage the generation process of sustainable ideas. In this paper we propose first an identification of these requirements to develop efficient eco-innovation tools with a special focus on the idea generation phase. Then we present one experimental test that was done to compare different eco-design and creativity tools: ASIT, eco compass, LiDS Wheel and ecoASIT. The ecoASIT tool is the result of our adaptation of the ASIT creative method to stimulate ideas in the field of the sustainable development. To conclude, the paper describes the different perspectives for the exploitation of the test.
Peptide Bond Formation between Aminoacyl-Minihelices by a Scaffold Derived from the Peptidyl Transferase Center

The peptidyl transferase center (PTC) in the ribosome is composed of two symmetrically arranged tRNA-like units that contribute to peptide bond formation. We prepared units of the PTC components with putative tRNA-like structure and attempted to obtain peptide bond formation between aminoacyl-minihelices (primordial tRNAs, the structures composed of a coaxial stack of the acceptor stem on the T-stem of tRNA). One of the components of the PTC, P1c2UGGU (74-mer), formed a dimer and a peptide bond was formed between two aminoacyl-minihelices tethered by the dimeric P1c2UGGU. Peptide synthesis depended on both the existence of the dimeric P1c2UGGU and the sequence complementarity between the ACCA-3′ sequence of the minihelix. Thus, the tRNA-like structures derived from the PTC could have originated as a scaffold of aminoacyl-minihelices for peptide bond formation through an interaction of the CCA sequence of minihelices. Moreover, with the same origin, some would have evolved to constitute the present PTC of the ribosome, and others to function as present tRNAs.
Lightweight Path Recovery in IPv6 Internet-of-Things Systems

In an Internet-of-Things system supported by Internet Protocol version 6 (IPv6), the Routing Protocol for Low-Power and Lossy Networks (RPL) presents extensive applications in various network scenarios. In these novel scenarios characterized by the access of massive devices, path recovery, which reconstructs the complete path of the packet transmission, plays a vital role in network measurement, topology inference, and information security. This paper proposes a Lightweight Path recovery algorithm (LiPa) for multi-hop point-to-point communication. The core idea of LiPa is to make full use of the spatial and temporal information of the network topology to recover the unknown paths iteratively. Specifically, spatial and temporal information refer to the potential correlations between different paths within a time slot and path status during different time slots, respectively. To verify the effect of our proposal, we separately analyze the performance of leveraging temporal information, spatial information, and their composition by extensive simulations. We also compare LiPa with two state-of-the-art methods in terms of the recovery accuracy and the gain–loss ratio. The experiment results show that LiPa significantly outperforms all its counterpart algorithms in different network settings. Thus, LiPa can be considered as a promising approach for packet-level path recovery with minor loss and great adaptability.