Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Theranostic Potential of Graphene Quantum Dots in Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the leading causes of death worldwide, with no definitive diagnosis or known cure. The aggregation of Tau protein into neurofibrillary tangles (NFTs), which contain straight filaments (SFs) and paired helical filaments (PHFs), is a major hallmark of AD. Graphene quantum dots (GQDs) are a type of nanomaterial that combat many of the small-molecule therapeutic challenges in AD and have shown promise in similar pathologies. In this study, two sizes of GQDs, GQD7 and GQD28, were docked to various forms of Tau monomers, SFs, and PHFs. From the favorable docked poses, we simulated each system for at least 300 ns and calculated the free energies of binding. We observed a clear preference for GQD28 in the PHF6 (306VQIVYK311) pathological hexapeptide region of monomeric Tau, while GQD7 targeted both the PHF6 and PHF6* (275VQIINK280) pathological hexapeptide regions. In SFs, GQD28 had a high affinity for a binding site that is available in AD but not in other common tauopathies, while GQD7 behaved promiscuously. In PHFs, GQD28 interacted strongly near the protofibril interface at the putative disaggregation site for epigallocatechin-3-gallate, and GQD7 largely interacted with PHF6. Our analyses revealed several key GQD binding sites that may be used for detecting, preventing, and disassembling the Tau aggregates in AD.
Deep reinforcement learning-based path planning for net sealing robot

For the path planning problem of overhead transmission net sealing robot in the process of sealing network, a visual perception and decision method based on deep reinforcement learning is proposed. By combining the perceptual capability of convolutional neural networks with the decision-making capability of reinforcement learning, the method achieves direct output control from the visual perception input of the environment to the action through end-to-end learning, forming a closed loop between the system environment perception and decision control directly, and obtaining the optimal decision strategy by maximizing the cumulative reward return of the robot's interaction with the dynamical environment. Simulation experimental results prove that the method can meet the requirements of multi-task intelligent perception and decision making, and better solve the problems of traditional algorithms such as easily falling into local optimum, oscillating in narrow passages and unreachable targets near obstacles, which greatly improve the real-time and adaptability of trajectory tracking and dynamic obstacle avoidance of the net sealing robot and ensure the safe operation of the net sealing robot in transmission line sealing operations.
Application of machine learning on optical fibre distributed sensing for power line applications

We present a study on the application of machine learning to optical fibre distributed sensing, with data recovered using a state-of-the-art, commercial BOTDR distributed sensing system; temperature information was extracted from the power line distribution networks that are part of the Electricity Authority of Cyprus. A machine learning approach was implemented for the prediction task of finding points of abnormal behaviour, mimicking the power cable joints that are prone to failure, along with general monitoring for unusual behaviour and potential cable fault conditions; the task is a binary classification one. Labels “0/1” were assigned to the BOTDR measurements, with “1” corresponding to data points in space and time for which the signal showcased a problematic scenario, such as that recorded by optical fibres that are collocated with power cables where the fibre’s temperature measurement increases to dangerously high values, and conversely “0” for all other scenarios. The algorithm’s base is a variation of the state-of-the-art transformer architecture, which depends solely on attention mechanisms. The field data recovered show the potential of the algorithm to predict spatiotemporally problematic points, using the temperature measurements of the collocated fibre.
Riok1, A Novel Potential Target in MSI-High p53 Mutant Colorectal Cancer Cells

The vulnerabilities of cancer cells constitute a promising strategy for drug therapeutics. This paper integrates proteomics, bioinformatics, and cell genotype together with in vitro cell proliferation assays to identify key biological processes and potential novel kinases that could account, at least in part, for the clinical differences observed in colorectal cancer (CRC) patients. This study started by focusing on CRC cell lines stratified by their microsatellite (MS) state and p53 genotype. It shows that cell-cycle checkpoint, metabolism of proteins and RNA, signal transduction, and WNT signaling processes are significantly more active in MSI-High p53-WT cell lines. Conversely, MSI-High cell lines with a mutant (Mut) p53 gene showed hyperactivation of cell signaling, DNA repair, and immune-system processes. Several kinases were linked to these phenotypes, from which RIOK1 was selected for additional exploration. We also included the KRAS genotype in our analysis. Our results showed that RIOK1’s inhibition in CRC MSI-High cell lines was dependent on both the p53 and KRAS genotypes. Explicitly, Nintedanib showed relatively low cytotoxicity in MSI-High with both mutant p53 and KRAS (HCT-15) but no inhibition in p53 and KRAS WT (SW48) MSI-High cells. This trend was flipped in CRC MSI-High bearing opposite p53-KRAS genotypes (e.g., p53-Mut KRAS-WT or p53-WT KRAS-Mut), where observed cytotoxicity was more extensive compared to the p53-KRAS WT-WT or Mut-Mut cells, with HCT 116 (KRAS-Mut and p53-WT) being the most sensitive to RIOK1 inhibition. These results highlight the potential of our in silico computational approach to identify novel kinases in CRC sub-MSI-High populations as well as the importance of clinical genomics in determining drug potency.
COVID-19 Pandemic and Changes in Children’s Behavioral Problems: The Mediating Role of Maternal Depressive Symptoms

The COVID-19 pandemic has been linked to a range of behavioral problems in children. To date, however, longitudinal studies with data prior to the pandemic are rare, and moreover, few studies have examined the family context. This is notable as evidence suggests that mothers were highly vulnerable to the effects of the pandemic, and stress proliferation models would argue that children’s wellbeing are undoubtedly affected by maternal wellbeing. In the current investigation, we examine changes in maternal depressive symptoms and children’s behavioral problems from prior to the pandemic to the first few months of COVID-19 in the U.S. The results suggest a significant increase in children’s internalizing problems and maternal depressive symptoms. Consistent with stress proliferation models, the relationship between COVID-19-related stressors and children’s behavioral problems were mediated by maternal mental health.
The impact of demographic information on students’ preferable extracurricular activities for English learning

The study aims to explore the potential of extracurricular activities (ECAs) in enhancing English learning among Vietnamese higher education students, given the government’s emphasis on English language reforms. In Southwest Vietnam, a comprehensive survey consisting of 20 questions was carried out among 1,032 students at a higher education establishment. The aim was to ascertain their favored ECAs and to explore how students’ demographic details, particularly their gender, field of study, and academic year, influenced these preferences. Results showed students were fond of ECAs for English learning, with female students expressing a higher preference for participation. Students’ majors and years in university significantly influenced preferences for specific activities, while no significant difference was found in the overall perception of ECAs. The study concludes that understanding students’ preferences and demographic influences can help design more effective ECAs for English learning in Vietnamese higher education institutions. The findings offer insights for developing tailored ECAs to address students’ unique needs and preferences, thereby improving English language learning outcomes in Vietnamese higher education institutions.
Structural and electronic properties of the Au(001)/Fe(001) interface from density functional theory calculations

A density functional theory (DFT) investigation of the structural and electronic properties of the Au(001)/Fe(001) interface, as a function of the number of Au layers deposited on the Fe substrate (from 1 to 11 Au monolayers), is presented. The elastic effects on the interface properties due to the lattice mismatch between Fe and Au, calculated by DFT using the generalized gradient approximation, are also evaluated. At the interface, the interlayer distances in the Fe substrate as well as in the Au slab expand. The Fe atoms of the interface exhibit an enhanced magnetic moment and the Au atoms of the interface bear a nonzero (but very low) magnetic moment. The calculated interface energy favors the formation of core-shell Fe@Au nanoparticles, where Au(001) is in epitaxy at 45 - on (001) facets of a Fe nanocube. Finally, the analysis of the electronic properties shows that the work of adhesion of the interface is maximum for a coating of Fe with 2 Au monolayers, which can be explained by a strong overlap between the electronic densities of the Fe interface atoms with those of the Au surface atoms. © 2012 American Physical Society.
Timing and Predictors of Recanalization After Anticoagulation in Cerebral Venous Thrombosis

Background and Purpose Vessel recanalization after cerebral venous thrombosis (CVT) is associated with favorable outcomes and lower mortality. Several studies examined the timing and predictors of recanalization after CVT with mixed results. We aimed to investigate predictors and timing of recanalization after CVT. Methods We used data from the multicenter, international AntiCoagulaTION in the Treatment of Cerebral Venous Thrombosis (ACTION-CVT) study of consecutive patients with CVT from January 2015 to December 2020. Our analysis included patients that had undergone repeat venous neuroimaging more than 30 days after initiation of anticoagulation treatment. Prespecified variables were included in univariate and multivariable analyses to identify independent predictors of failure to recanalize. Results Among the 551 patients (mean age, 44.4±16.2 years, 66.2% women) that met inclusion criteria, 486 (88.2%) had complete or partial, and 65 (11.8%) had no recanalization. The median time to first follow-up imaging study was 110 days (interquartile range, 60–187). In multivariable analysis, older age (odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03–1.07), male sex (OR, 0.44; 95% CI, 0.24–0.80), and lack of parenchymal changes on baseline imaging (OR, 0.53; 95% CI, 0.29–0.96) were associated with no recanalization. The majority of improvement in recanalization (71.1%) occurred before 3 months from initial diagnosis. A high percentage of complete recanalization (59.0%) took place within the first 3 months after CVT diagnosis. Conclusion Older age, male sex, and lack of parenchymal changes were associated with no recanalization after CVT. The majority recanalization occurred early in the disease course suggesting limited further recanalization with anticoagulation beyond 3 months. Large prospective studies are needed to confirm our findings.
Recent development of TlBr gamma-ray detectors

Planar detectors, strip detectors and double-sided strip detectors were fabricated from TlBr crystals grown by the traveling molten zone method using zone-purified material. The detector performance including the leakage current, energy resolutions and timing performance were evaluated in order to assess the capability of the detectors for PET and SPECT applications. The TlBr detectors exhibited excellent spectroscopic performance at room temperature. An energy resolution of 3.4% FWHM at 511 keV was obtained from a TlBr planar detector 1 mm thick. A TlBr strip detector 1 mm thick with four anode strip electrodes exhibited almost uniform detector performance over the strips with the average energy resolution of 4.4% FWHM at 511 keV. A TlBr double-sided strip detector exhibited an energy resolution of 6.3% FWHM for 122 keV gamma-rays. Coincidence timing spectra between a TlBr planar detector and a BaF2 scintillation detector were recorded at room temperature. Timing resolutions of 14 ns and 24 ns were obtained from TlBr detectors 0.5 mm and 1 mm thick, respectively. By cooling the detector to 0 ºC, an improved timing resolution of 12 ns was obtained from a TlBr detector 1 mm thick.
RANCANGAN D-OPTIMAL MODEL MICHAELIS MENTEN DAN EMAX DENGAN MATLAB

Michaelis Menten and Emax models are widely used in chemistry, pharmacokinetics and pharmacodynamics areas. D-optimal criteria is criteria with the purpuse to minimize the variance of the estimator of parameters in the model. In this paper will discuss the D-optimal design for Michaelis Menten and Emax models with homoscedastics error assumtion. Determination of D-optimal designs based on Generalied Equivalence Theorem Kiefer-Wolvowitz. We used minimally supported design with the proportion of each design point is uniform, lower bound of design region is design point and the others are interior points. Keywords: D-optimal, Michaelis Menten, Emax, Minimally Supported Design, Homoscedastics
Optimizing expected policy outcomes through creative problem structuring: Police human resource development policy in the southeast Sulawesi regional police, Indonesia

This study aims to investigate the problem structuring process using creative methods to create well-structured problems, and how it can optimize the expected policy outcomes of human resource development in regional police. This study uses a qualitative research method by integrating qualitative data from semi-structured interviews and administrative data. Five informants were purposively selected, four of whom were authorized officials in the field of human resource development at regional police institutions, and the other was a prominent civic organization activist. The interview data were analysed using qualitative interactive models, while directed content analysis was applied to administrative data. The results of the study show that the problem structuring process in human resource development policies for regional police is limited to identifying the main elements of problem definition through the partial use of brainstorming methods and classification analysis. Such a problem structuring process cannot create well-structured problems that optimize expected policy outcomes of human resource development. However, the use of creative problem-solving methods in a comprehensive manner can optimize the expected policy outcomes from the development of more critical and collaborative human resource development in the regional police force.
Audio Compression using a Modified Vector Quantization algorithm for Mastering Applications

— Audio data compression is used to reduce the transmission bandwidth and storage requirements of audio data. It is the second stage in the audio mastering process with audio equalization being the first stage. Compression algorithms such as BSAC, MP3 and AAC are used as standards in this paper. The challenge faced in audio compression is compressing the signal at low bit rates. The previous algorithms which work well at low bit rates cannot be dominant at higher bit rates and vice-versa. This paper proposes an altered form of vector quantization algorithm which produces a scalable bit stream which has a number of fine layers of audio fidelity. This modified form of the vector quantization algorithm is used to generate a perceptually audio coder which is scalable and uses the quantization and encoding stages which are responsible for the psychoacoustic and arithmetical terminations that are actually detached as practically all the data detached during the prediction phases at the encoder side is supplemented towards the audio signal at decoder stage. Therefore, clearly the quantization phase which is modified to produce a bit stream which is scalable. This modified algorithm works well at both lower and higher bit rates. Subjective evaluations were done by audio professionals using the MUSHRA test and the mean normalized scores at various bit rates was noted and compared with the previous algorithms.
Relationship satisfaction and psychological wellbeing in men having participated in the MindTheHeart® psychoeducation intervention after an acute coronary syndrome

This study investigated the associations of relationship satisfaction, depression, and anxiety post-acute coronary syndrome in a sample of men having completed an early, couple-focused, and men-tailored psychoeducation intervention. A baseline sample of 50 male patients were followed over a 12-month period and completed the Couples Satisfaction Index and the Depression, Anxiety and Stress Scale. Results revealed a significant improvement in depression symptoms from T0 to T1, during which all participants completed the MindTheHeart® psychoeducation intervention. In addition, a hierarchical regression analysis showed that relationship satisfaction is an important predictor of this improvement in depression symptoms. Our preliminary findings point to the consideration of life partners in post-ACS interventions and encourage the exploration of this clinical approach in future research.
Dental Anomalies in Maxillary Incisors and Canines among Patients with Total Cleft Lip and Palate

Cleft lip and palate is the most common asymmetric congenital condition of the orofacial region, which also finds its reflection in dental anomalies. The aim of the study was to present the dental asymmetries of the front region of the maxilla in cleft patients. Materials and Methods: We analyzed plaster casts and panoramic X-rays of 154 patients with total clefts and 151 healthy individuals. The cleft patients’ age ranged between 7.1 and 20 years (mean 13.18). The control group had a similar age range (7.1 and 20 years, mean 13.44). The digital caliper was used to measure the width of the teeth. Each measurement was performed three times each. Results: Most of the dental anomalies among cleft patients referred to the lateral incisors and were focused on the cleft side. The asymmetry of the incisors is reflected both in the number of teeth in the cleft region and their width. The lateral incisor was missing twice as frequently on the cleft side of the individual. If present, the lateral incisor was usually ±1.5 mm narrower than the incisor on the opposite side. In bilateral clefts, dental anomalies occurred more frequently on the left side. Conclusions: Dental problems occurred more frequently in patients with total cleft lip and palate than in healthy individuals. The most commonly affected teeth were the lateral incisors. The width of the lateral incisors was reduced in cleft patients—showing a smaller mesiodistal dimension on the cleft side.
Establishing class size policies for optimal course outcomes in online undergraduate education programs

ABSTRACT The long-term shift toward online learning over the past two decades was greatly accelerated by COVID-19, heightening the urgency in higher education for policies meant to better optimize online environments for student learning. The determination of class sizes for online courses is one such policy. In this hierarchical linear regression of undergraduate students’ grades in online courses (n = 6,615), we found a significant but negligible positive relationship between section size and students’ final grades. Among other findings, we found little evidence to conclude that class size policies have any substantive relationship to students’ end-of-course grades in online environments.
An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection

Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one’s own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice.
Numerical Filtering of Spurious Transients in a Satellite Scanning Radiometer: Application to CERES

Abstract The Clouds and Earth Radiant Energy System (CERES) scanning radiometer was designed to provide high accuracy measurements of the radiances from the earth. Calibration testing of the instruments showed the presence of an undesired slow transient in the measurements of all channels at 1% to 2% of the signal. Analysis of the data showed that the transient consists of a single linear mode. The characteristic time of this mode is 0.3 to 0.4 s and is much greater than that the 8–10-ms response time of the detector, so that it is well separated from the detector response. A numerical filter was designed for the removal of this transient from the measurements. Results show no trace remaining of the transient after application of the numerical filter. The characterization of the slow mode on the basis of ground calibration data is discussed and flight results are shown for the CERES instruments aboard the Tropical Rainfall Measurement Mission and Terra spacecraft. The primary influence of the slow mode is.
Galleria mellonella—A Model for the Study of aPDT—Prospects and Drawbacks

Galleria mellonella is a promising in vivo model insect used for microbiological, medical, and pharmacological research. It provides a platform for testing the biocompatibility of various compounds and the kinetics of survival after an infection followed by subsequent treatment, and for the evaluation of various parameters during treatment, including the host–pathogen interaction. There are some similarities in the development of pathologies with mammals. However, a limitation is the lack of adaptive immune response. Antimicrobial photodynamic therapy (aPDT) is an alternative approach for combating microbial infections, including biofilm-associated ones. aPDT is effective against Gram-positive and Gram-negative bacteria, viruses, fungi, and parasites, regardless of whether they are resistant to conventional treatment. The main idea of this comprehensive review was to collect information on the use of G. mellonella in aPDT. It provides a collection of references published in the last 10 years from this area of research, complemented by some practical experiences of the authors of this review. Additionally, the review summarizes in brief information on the G. mellonella model, its advantages and methods used in the processing of material from these larvae, as well as basic knowledge of the principles of aPDT.