Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondria-Targeted, Nanoparticle-Based Drug-Delivery Systems: Therapeutics for Mitochondrial Disorders

Apart from ATP generation, mitochondria are involved in a wide range of functions, making them one of the most prominent organelles of the human cell. Mitochondrial dysfunction is involved in the pathophysiology of several diseases, such as cancer, neurodegenerative diseases, cardiovascular diseases, and metabolic disorders. This makes it a target for a variety of therapeutics for the diagnosis and treatment of these diseases. The use of nanoparticles to target mitochondria has significant importance in modern times because they provide promising ways to deliver drug payloads to the mitochondria by overcoming challenges, such as low solubility and poor bioavailability, and also resolve the issues of the poor biodistribution of drugs and pharmacokinetics with increased specificity. This review assesses nanoparticle-based drug-delivery systems, such as liposomes, DQAsome, MITO-Porters, micelles, polymeric and metal nanocarriers, as well as quantum dots, as mitochondria-targeted strategies and discusses them as a treatment for mitochondrial disorders.
Consumer Perception, Online Retailing and the Nigerian Experience: An Interchange of Demographic Patterns and Service Quality

Customer perception has earned a lot of interest in recent times as a tool for assessing service quality in service-oriented organizations. Although numerous studies have addressed customers’ perceptions of service quality in traditional service settings, a relatively small volume of literature has empirically examined service quality issues in the online retailing environment. In the case of Nigeria, there is no known research relating to perceived service quality of online retailers. To address this knowledge gap, the consumer perception of the online retail sector in Nigeria was studied. The expectation theory, the electronic service quality model, and the electronic recovery service quality model served as the theoretical foundations on which this study is based. The methodology involved the use of a descriptive survey method to assess the perceived service quality of the online retail sector and the effect of selected demographic factors on customer perception. The study was conducted with a sample size of 300 respondents. The results revealed that the perceived service quality of the online retail sector in Nigeria is above average but has room for improvement. The study also showed that customer perception was not affected by the demographic factors that this research focused on.
Multi-Agent Reinforcement Learning for Cooperative Task Offloading in Distributed Edge Cloud Computing

SUMMARY Distributed edge cloud computing is an important computation infrastructure for Internet of Things (IoT) and its task o ﬄ oading problem has attracted much attention recently. Most existing work on task o ﬄ oading in distributed edge cloud computing usually assumes that each self-interested user owns one edge server and chooses whether to execute its tasks locally or to o ﬄ oad the tasks to cloud servers. The goal of each edge server is to maximize its own interest like low delay cost, which corresponds to a non-cooperative setting. However, with the strong development of smart IoT communities such as smart hospital and smart factory, all edge and cloud servers can belong to one organization like a technology company. This corresponds to a cooperative setting where the goal of the organization is to maximize the team interest in the overall edge cloud computing system. In this paper, we consider a new problem called cooperative task o ﬄ oading where all edge servers try to cooperate to make the entire edge cloud computing system achieve good performance such as low delay cost and low energy cost. However, this problem is hard to solve due to two issues: 1) each edge server status dynamically changes and task arrival is uncertain; 2) each edge server can observe only its own status, which makes it hard to optimize team interest as global information is unavailable. For solving these issues, we formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) which can well handle the dynamic features under partial observations. Then, we apply a multi-agent reinforcement learning algorithm called value decomposition network (VDN) and propose a VDN-based task o ﬀ loading algo-rithm (VDN-TO) to solve the problem. Speciﬁcally, the motivation is that we use a team value function to evaluate the team interest, which is then divided into individual value functions for each edge server. Then, each edge server updates its individual value function in the direction that can maximize the team interest. Finally, we choose a part of a real dataset to evaluate our algorithm and the results show the e ﬀ ectiveness of our algorithm in a comparison with some other existing methods.
Electronic Learning Material of Newton’s Laws with Kvisoft Flipbook Maker to Improve Learning Outcomes of Students

In the world of education, teachers can use technology in learning activities in learning material. The fact found in schools is that the learning material used by students are still low in the use of technology. This indirectly has an impact on student learning outcomes which are still low. One solution that can be done is to apply Newton's law electronics learning material with Kvisoft Flipbook. This study aimed to improve the learning outcomes of class X students of SMAN 1 Payakumbuh. This research is a quasi-experiment with a randomized posttest only control group design. The sampling technique used purposive sampling with random cluster sampling. The data collection instrument consisted of a written test sheet for the knowledge aspect, an observation sheet for the attitude aspect, and a performance appraisal sheet for the skills aspect. From the results of data analysis, it can be stated that applying Newton's law electronics learning material with Kvisoft Flipbook Maker can improve learning outcomes in the knowledge, attitudes, and skills of class X students of SMAN 1 Payakumbuh with a 95% confidence level.
Thyroid Hormone Receptor Isoforms Alpha and Beta Play Convergent Roles in Muscle Physiology and Metabolic Regulation

Skeletal muscle is a key energy-regulating organ, skilled in rapidly boosting the rate of energy production and substrate consumption following increased workload demand. The alteration of skeletal muscle metabolism is directly associated with numerous pathologies and disorders. Thyroid hormones (THs) and their receptors (TRs, namely, TRα and TRβ) exert pleiotropic functions in almost all cells and tissues. Skeletal muscle is a major THs-target tissue and alterations of THs levels have multiple influences on the latter. However, the biological role of THs and TRs in orchestrating metabolic pathways in skeletal muscle has only recently started to be addressed. The purpose of this paper is to investigate the muscle metabolic response to TRs abrogation, by using two different mouse models of global TRα- and TRβKO. In line with the clinical features of resistance to THs syndromes in humans, characterized by THRs gene mutations, both animal models of TRs deficiency exhibit developmental delay and mitochondrial dysfunctions. Moreover, using transcriptomic and metabolomic approaches, we found that the TRs–THs complex regulates the Fatty Acids (FAs)-binding protein GOT2, affecting FAs oxidation and transport in skeletal muscle. In conclusion, these results underline a new metabolic role of THs in governing muscle lipids distribution and metabolism.
Anti PD-1/Anti PDL-1 Inhibitors in Advanced Gastroesophageal Cancers: A Systematic Review and Meta-Analysis of Phase 2/3 Randomized Controlled Trials

Importance: Immune checkpoint inhibitors (ICI) have revolutionized the treatment for gastroesophageal cancers (GEC). It is important to investigate the factors that influence the response to anti-PD-1/PD-L1 ICIs. Objective: To assess the benefits of PD-1/PD-L1 ICIs in advanced GEC and perform subgroup analysis to identify patient populations who would benefit from ICI. Data sources: PubMed, Embase, Scopus, and the Cochrane Library databases were systematically searched from database inception to September 2021 for all relevant articles. We also reviewed abstracts and presentations from all major conference proceedings including relevant meetings of the American Society of Clinical Oncology (ASCO), and the European Society for Medical Oncology (ESMO) during the last four years (2018 to 2021) and reviewed citation lists. Study selection, data extraction, and synthesis: Full articles and presentations were further assessed if the information suggested that the study was a phase 2/3 randomized controlled trial (RCT) comparing PD-1/PD-L1 inhibitor either alone, or in combination with standard therapy vs. standard therapy in advanced GEC. The full text of the resulting studies/presentations and extracted data were reviewed independently according to PRISMA guidelines. Main outcomes and measures: The main outcomes were OS, PFS, and treatment-related adverse events (TRAEs). Results: A total of 168 studies were assessed for eligibility, and 17 RCTs with 12,312 patients met the inclusion criteria. There was an OS benefit in the overall population with ICIs (HR 0.78; 95% CI 0.73–0.83 p < 0.001). Immunotherapy showed better OS benefit in males (HR 0.77 95% CI 0.72–0.83; p < 0.001) than females (HR 0.89; 95% CI 0.80–0.99 p < 0.03), esophageal primary tumors (HR 0.70 95% CI 0.64–0.76 p < 0.001) vs. gastric cancer (HR 0.84 95% CI 0.74–0.94 p 0.002) or GEJ cancer (HR 0.84 95% CI 0.72–0.98 p 0.024) and in squamous cell carcinoma (HR 0.71 95% CI 0.66–0.77 p < 0.001) vs. adenocarcinoma (HR 0.85 95% CI 0.78–0.93 p < 0.001). PD-L1 positive patients seemed to benefit more (HR 0.74 95% CI 0.67–0.82 p < 0.001) compared to PD-L1 negative patients (HR 0.86 95% CI 0.74–1.00 p < 0.043), and Asians showed OS benefit (HR 0.76 95% CI 0.67–0.87 p < 0.001) compared to their White counterparts (HR 0.92 95% CI 0.74–1.14; p 0.424). Conclusions and relevance: ICIs improve survival in advanced GEC without significantly increasing the side effects. However, certain subgroups of patients such as males, Asians, and those with esophageal primary, PD-L1 positive tumors and squamous cell carcinoma benefit more from such treatments. Further translational research is needed to understand the mechanistic links and develop new biomarkers.
A Semantic-Based Dual Location Privacy-Preserving Approach

SUMMARY With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Di ﬀ erent from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.
A Real-Time Bidding Gamification Service of Retailer Digital Transformation

Real-time bidding (RTB), which holds bids for advertisers to choose when and where to display their advertisements with desired budgets, is one way to improve effectiveness. We proposed supply side platform (SSP) and demand side platform (DSP) to integrate supplier side and demand side as solution. The proposed method combines two digital technology service, gamified RTB and mobile location-based analysis (LBA). In order to make target customers make interactions actively with the advertisement contents, the idea of gamification is employed to increase customer participation. By the coupons in the gamifying RTB App, it have increased the coupon usage rate of 16.7% and 11.6% in the restaurant and apparel industries. It is a successfully digital transformation marketing service for retail industries. The gamified RTB plays an important role in making old business circles accept new technologies for the renewal of promotion strategies and help find the business directions in the future.
Supplementation of guanidinoacetic acid and rumen-protected methionine increased growth performance and meat quality of Tan lambs

Objective Tan lambs (n = 36, 3 mo old, 19.1±0.53 kg) were used to assess effects of dietary guanidinoacetic acid (GAA) and rumen-protected methionine (RPM) on growth performance, carcass traits, meat quality, and serum parameters. Methods Lambs were randomly assigned to three treatment groups, with 6 pens per group and 2 lambs per pen. Dietary treatments were: basal diet alone (I); basal diet supplemented with 0.08% GAA+0.06% RPM (II); and basal diet supplemented with 0.08% GAA+0.08% RPM (III). Diets were provided three times a day for 90 d. Intake per pen was recorded daily and individual lamb body weight (BW) was measured monthly. Carcass traits were measured after slaughter and meat quality at the end of the experiment, blood samples were taken on a subgroup of lambs for analysis of indicators mostly related to protein metabolism. Results Final BW and average daily gain for the first and second month, and for the entire experiment were greater in Treatment II compared to Treatment I (p<0.05), whereas feed to gain ratio was lower (p<0.05). Treatment II had the optimal dressing percentage and net meat weight proportion, as well as crude protein and intramuscular fat concentrations in muscles. Treatment II improved meat quality, as indicated by the greater water holding capacity, pH after 45 min and 48 h, and lower shear force and cooking loss. Dietary supplementation of GAA and RPM also increased the meat color a* and b* values at 24 h. Finally, Treatment II increased total protein, and serum concentrations of albumin and creatinine, but decreased serum urea nitrogen concentrations, indicating improved protein efficiency. Conclusion In this study, 0.08% GAA+0.06% RPM supplementation improved growth performance and meat quality of Tan lambs.
Bacteria Strain Identification with Fluorescence Spectra

From the research of fluorescence spectra of lactobacillus acidophilus, streptococcus mutans and lactobacillus bulgaricus, it was indicated that under the stimulation of UV light, the prbiotic solution would emit fluorescence. Under the stimulation of optimal excitation wavelength of 290nm, the fluorescence peak fell in the range of 300-650nm. 150 groups of spectral data measured by wavelet transform were used for compression, after which each group of data reduced from 1341 points originally to 168 points, which not only retained the features of the original spectrum, but also increased the processing speed of neural networks. Radial basis function neural network method was used for compressed data research, 40 groups of experimental data were trained for each strain, based on which 30 groups of unknown data were identified. Results showed that after training, the radial basis function neural network could accurately predict unknown strains.
ASSESSING THE IMPACT OF PATENT ATTRIBUTES ON THE VALUE OF DISCRETE AND COMPLEX INNOVATIONS

This study assesses the degree to which the social value of patents can be connected to the private value of patents across discrete and complex innovation. The underlying theory suggests that the social value of cumulative patents is less related to the private value of patents. We use the patents applied between 1995 and 2002 and granted on or before December 2018 from the Indian Patent Office (IPO). Here, the patent renewal information is utilised as a proxy for the private value of the patent. We have used a variety of logit regression model for the impact assessment analysis. The results reveal that the technology classification (i.e., discrete vs. complex innovations) plays an important role in patent value assessment, and some technologies are significantly different than the others even within the two broader classifications. Moreover, the non-resident patents in India are more likely to have a higher value than the resident patents. According to the conclusions of this study, only a few technologies from the discrete and complex innovation categories have some private value. There is no evidence that patent social value indicators are less useful in complicated technical classes than in discrete ones.
Role of Psychological Capital as a Mediator between Authentic Leadership and Learning Organization in Bangkok Metropolitan Schools: A Causal Relationship Model

The purpose of the research was to develop the empirical causal relationship model of learning organization, with psychological capital as a mediator, impacted by authentic leadership in Bangkok Metropolitan school, Thailand. The literature review was extensively conducted to identify the research hypothesized model, and the causal relationship modelling was implemented by 1,080 teachers. Descriptive and inferential statistics and structural equation modelling were conducted to empirically investigate causal relationships. The findings revealed that the causal relationship model fit the data well, as indicated by fit indices; Chi-square indicated at 26.464 (df=34; p=0.818), with non-statistical significance. Model consistency was identified by Goodness of Fit Index (GFI) at 0.997, Adjusted Goodness of Fit Index (AGFI) at 0.988, Root Mean Square Residual (RMR) at 0.00519, and Root Mean Square Error of Approximation (RMSEA) at 0.000. The findings consistently and significantly revealed direct and indirect effects of authentic leadership on learning organization as mediated by psychological capital, which identified an indirect effect (0.56**) and a total effect (0.65**). The direct effects of authentic leadership were the highest impact on psychological capital (0.81**), though with a non-significant direct impact on learning organization (0.08). The results significantly indicated a direct effect of psychological capital on learning organization (0.69**), verified psychological capital as an effective mediator enhancing learning organization. By square multiple correlations of the latent variables, psychological capital was explained and predicated by authentic leadership for 66%, and learning organization was explained and predicated by authentic leadership and psychological capital for 58%.
Model Predictive Control-Based Collision Avoidance for Autonomous Surface Vehicles in Congested Inland Waters

Compared with open waters, congested inland waters have narrow waterways, many river-crossing bridges, a high density of navigation, and high current velocity in some sections. In this study, an improved collision avoidance algorithm based on model predictive control (MPC) is proposed to solve the problem of collision avoidance for autonomous surface vehicles (ASVs) in congested inland waters. First, considering the influence of current, the collision avoidance problem of ASVs is transformed into a nonlinear programming problem, and the kinematics of ASVs and the boundary of the channel are regarded as its inequality constraints. Next, since ASVs cannot perform large-scale collision avoidance in congested inland waters, the strategy of reducing the speed and slightly changing the yaw angle is adopted to realize collision avoidance. Then, an improved dynamic bumper model is used to model the safe zone of ASVs and dynamic obstacles, which improves the efficiency of the algorithm and the safety of ASVs. Finally, the collision avoidance rules and the evaluation function of the collision avoidance maneuver are constructed in the cost function of the algorithm. The simulation experiments in different encounter scenarios show that the proposed algorithm significantly improves the rationality and compliance of ASVs’ autonomous collision avoidance in congested inland waters.
Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study

Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learning is an efficient analytical strategy to simultaneously identify multiple candidate biomarkers for dementia. We aimed to identify important candidate blood biomarkers for dementia using three machine learning models. We included 1642 (mean 69 ± 6 yr, 53% women) dementia-free Framingham Offspring Cohort participants attending examination, 7 who had available blood biomarker data. We developed three machine learning models, support vector machine (SVM), eXtreme gradient boosting of decision trees (XGB), and artificial neural network (ANN), to identify candidate biomarkers for incident dementia. Over a mean 12 ± 5 yr follow-up, 243 (14.8%) participants developed dementia. In multivariable models including all 38 available biomarkers, the XGB model demonstrated the strongest predictive accuracy for incident dementia (AUC 0.74 ± 0.01), followed by ANN (AUC 0.72 ± 0.01), and SVM (AUC 0.69 ± 0.01). Stepwise feature elimination by random sampling identified a subset of the nine most highly informative biomarkers. Machine learning models confined to these nine biomarkers showed improved model predictive accuracy for dementia (XGB, AUC 0.76 ± 0.01; ANN, AUC 0.75 ± 0.004; SVM, AUC 0.73 ± 0.01). A parsimonious panel of nine candidate biomarkers were identified which showed moderately good predictive accuracy for incident dementia, although our results require external validation.
Design, Synthesis and Biological Evaluation of N-phenylindole Derivatives as Pks13 Inhibitors againstMycobacterium tuberculosis

Polyketide synthase 13 (Pks13), an essential enzyme for the survival of Mycobacterium tuberculosis (Mtb), is an attractive target for new anti-TB agents. In our previous work, we have identified 2-phenylindole derivatives against Mtb. The crystallography studies demonstrated that the two-position phenol was solvent-exposed in the Pks13-TE crystal structure and a crucial hydrogen bond was lost while introducing bulkier hydrophobic groups at indole N moieties. Thirty-six N-phenylindole derivatives were synthesized and evaluated for antitubercular activity using a structure-guided approach. The structure–activity relationship (SAR) studies resulted in the discovery of the potent Compounds 45 and 58 against Mtb H37Rv, with an MIC value of 0.0625 μg/mL and 0.125 μg/mL, respectively. The thermal stability analysis showed that they bind with high affinity to the Pks13-TE domain. Preliminary ADME evaluation showed that Compound 58 displayed modest human microsomal stability. This report further validates that targeting Pks13 is a valid strategy for the inhibition of Mtb and provides a novel scaffold for developing leading anti-TB compounds.