Miracle-dz/UniFAR-FTUs huggingface dataset:
The All-on-4 Concept Using Polyetheretherketone (PEEK)—Acrylic Resin Prostheses: Follow-Up Results of the Development Group at 5 Years and the Routine Group at One Year

Background: It is necessary to investigate the application of polymer materials in implant dentistry. The aim of this study was to examine the outcome of full-arch polyetheretherketone (PEEK)—acrylic resin implant-supported prostheses. Methods: Seventy-six patients were rehabilitated consecutively with 100 full-arch implant-supported prostheses of PEEK–acrylic resin (a development group (DG): 37 patients with 5 years of follow-up; a routine group (RG): 39 patients with 1 year of follow-up). The primary outcome measure was prosthetic survival. Secondary outcome measures were implant survival, marginal bone loss, biological complications, prosthetic complications, veneer adhesion, plaque levels, bleeding levels, and a patient subjective evaluation (including the Oral Health Impact Profile for the RG). Results: In both groups, prosthetic (DG: 93.6%; RG: 100%) and implant survival (DG: 98.9%; RG: 99.5%) were high, and marginal bone loss was low (DG: 0.54 mm; RG: 0.28 mm). The veneer adhesion rate was 28.6% of prostheses in DG (RG = 0%). Mechanical complications occurred in 49% and 11.8% of prostheses in DG and RG, respectively. Biological complications, plaque, and bleeding levels were low in both groups. The subjective patient evaluation was excellent in both groups (8.6 < DG < 8.8; 9.3 < RG < 9.5; OHIP = 1.38). Conclusions: Within the limitations of this study, PEEK can be considered a viable prosthetic alternative.
Spectral-based thickness profiling of the corpus callosum enhances anomaly detection in fetal alcohol spectrum disorders

Introduction Fetal alcohol spectrum disorders (FASD) range from fetal alcohol syndrome (FAS) to non-syndromic forms (NS-FASD). The neuroanatomical consequences of prenatal alcohol exposure are mainly the reduction in brain size, but also focal abnormalities such as those of the corpus callosum (CC). We previously showed a narrowing of the CC for brain size, using manual measurement and its usefulness to improve diagnostic certainty. Our aim was to automate these measurements of the CC and identify more recurrent abnormalities in FAS subjects, independently of brain size reduction. Methods We developed a fast, automated, and normalization-free method based on spectral analysis to generate thicknesses of the CC continuously and at singular points (genu, body, isthmus, and splenium), and its length (LCC). We applied it on midsagittal section of the CC extracted from T1-anatomical brain MRI of 89 subjects with FASD (52 FAS, 37 NS-FASD) and 126 with typically development (6–20 y-o). After adjusting for batch effect, we compared the mean profiles and thicknesses of the singular points across the 3 groups. For each parameter, we established variations with age (growth charts) and brain size in the control group (scaling charts), then identified participants with abnormal measurements (<10th percentile). Results We confirmed the slimming of the posterior half of the CC in both FASD groups, and of the genu section in the FAS group, compared to the control group. We found a significant group effect for the LCC, genu, median body, isthmus, and splenium thicknesses (p < 0.05). We described a body hump whose morphology did not differ between groups. According to the growth charts, there was an excess of FASD subjects with abnormal LCC and isthmus, and of FAS subjects with abnormal genu and splenium. According to the scaling charts, this excess remained only for LCC, isthmus and splenium, undersized for brain size. Conclusion We characterized size-independent anomalies of the posterior part of the CC in FASD, with an automated method, confirming and extending our previous study. Our new tool brings the use of a neuroanatomical criterion including CC damage closer to clinical practice. Our results suggest that an FAS signature identified in NS-FASD, could improve diagnosis specificity.
Semantic Hierarchy-Aware Segmentation

Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world at multiple levels. However, such hierarchical reasoning ability of human perception remains largely unexplored in current literature of semantic segmentation. Existing works are often aware of flatten labels and distinguish all the semantic categories exclusively for each pixel. In this work, we instead address hierarchical semantic segmentation (HSS), with the aim of providing a structured, pixel-wise description of visual observation in terms of a class hierarchy. We devise Hssn, a general HSS framework that tackles two critical issues in this task: i) how to efficiently adapt existing hierarchy-agnostic segmentation networks to the HSS setting, and ii) how to leverage the class hierarchy to regularize HSS network learning. To address i), Hssn directly casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. To solve ii), Hssn first explores inherent properties of the hierarchy as a training objective, which enforces segmentation predictions to obey the hierarchy structure. Furthermore, with a set of hierarchy-induced margin constraints, Hssn efficiently reshapes the learned pixel embedding space, so as to generate hierarchy-aware pixel representations and facilitate structured segmentation eventually. Building upon Hssn, we further exploit the mutual exclusion relation between semantic labels and strengthen the margin based regularization strategy with more meaningful constrains, leading to Hssn+, a more effective framework for HSS. We conduct extensive experiments on six semantic segmentation datasets (i.e., Mapillary Vistas 2.0, Cityscapes, LIP, PASCAL-Person-Part, PASCAL-Part-58, and PASCAL-Part-108), with different class hierarchies, network architectures, and backbones, and the results confirm the generalization and superiority of our algorithms.
EVALUATION OF ENERGY BAND STRUCTURE OF HALF-HEUSLER ALLOY LiZnX (X = As, P, and Sb) USING FIRST PRINCIPLE CALCULATION

The evaluation of energy band structure plays a vital role in understanding the electronic properties of materials. This research, we investigate the energy band structure of Half-Heusler alloys LiZn(X = As, P, and Sb) using a first principle approach based on Density Functional Theory (DFT). These alloys are of particular interest due to their potential applications in thermoelectric and spintronics devices. The corresponding Density of States (DOS) for the tripartite compounds LiZnX (X=As, P, and Sb) have been calculated and the contributions of the Li, Zn, As, P and Sb orbital to the Density of States at ambient pressure. This also confirmed that LiZnX (X=As, P, and Sb) is a semi-conductor with a narrow band-gap between the occupied and unoccupied regions around the Fermi level. The orbitals Li -1s, As-4p, As- 4s, Zn-3d has the highest contributions. The dominant of the orbitals P-1s and P-2p before the Fermi- level and Zn-2p after the Fermi-level are observed. We observed the dominant of the orbitals Sb-1s, Sb-3d, Li-1s, Li-2s, Zn-3d shows weak hybridization and low contribution. This features indicates that the covalent bond between these two atoms is weak, and could be responsible for the mechanical instability observed in the calculation. Meanwhile the band structure calculated and presented has narrow band-gab of 0.625. 0.937 and 0.313 respectively for the tripartite compound LiZnX(X=As, P, and Sb) and its a direct band-gap semiconductor. The obtained energy band structures provide valuable information about the electronic properties of LiZn (X = As, P, and Sb) alloys. The presence of band gaps is crucial for thermoelectric applications, as it indicates the presence of regions where electrons and holes are confined, enabling efficient charge transport.
Influences of omnichannel integration and value congruence on customer patronage: the flow theory perspective

ABSTRACT Omnichannel retailers face the elusive challenge of maintaining customer patronage in the current competitive business environment. Drawing upon the flow theory and literature on omnichannel integration, this study investigated how different types of omnichannel integration (i.e. informational, transactional, and relational integration) influence customer patronage through the flow experience. Additionally, this study examined the potential moderating role of retailer-customer value congruence in the impact of omnichannel integration. We collected survey data from 292 omnichannel customers to test our research model. The results showed that the flow experience mediated the positive effects of transactional integration and relational integration on customer patronage. Additionally, value congruence positively moderated the effects of informational integration and relational integration on the flow experience. The implications and limitations of this study are further discussed.
A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.
Secrecy Energy Efficiency Maximization for Secure Unmanned-Aerial-Vehicle-Assisted Simultaneous Wireless Information and Power Transfer Systems

Unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) systems have recently gained significant attraction in internet-of-things (IoT) applications that have limited or no infrastructure. Specifically, the free mobility of UAVs in three-dimensional (3D) space allows us good-quality channel links, thereby enhancing the communication environment and improving performance in terms of achievable rates, latency, and energy efficiency. Meanwhile, IoT devices can extend their battery life by harvesting the energy following the SWIPT protocol, which leads to an increase in the overall system lifespan. In this paper, we propose a secure UAV-assisted SWIPT system designed to optimize the secrecy energy efficiency (SEE) of a ground network, wherein a base station (BS) transmits confidential messages to an energy-constrained device in the presence of a passive eavesdropper. Here, we employ a UAV acting as a helper node to improve the SEE of the system and to aid in the energy harvesting (EH) of the battery-limited ground device following the SWIPT protocol. To this end, we formulate the SEE maximization problem by jointly optimizing the transmit powers of the BS and UAV, the power-splitting ratio for EH operations, and the UAV’s flight path. The solution is obtained via a proposed algorithm that leverages successive convex approximation (SCA) and Dinkelbach’s method. Through simulations, we corroborate the feasibility and effectiveness of the proposed algorithm compared to conventional partial optimization approaches.
Influence of calcium, magnesium and iron ions on the molecular mass of exoproteins during biogranulation

In this study, we performed the qualitative analysis of exoproteins during granule formation in the presence or in the absence of cations. The staining of thin granule cryosections showed that nucleic acids, proteins, polysaccharides and calcium cations were the dominant components of the granules. Proteins are the structural components associated with calcium ions. We determined changes in the proteomic proﬁle and tightly bound extracellular polymeric substances (EPS) of the slime. The exopolymeric matrix containing the proteins was extracted using the Dowex resin method. Proteomic proﬁle was analysed by SDS-PAGE method (sodium dodecyl sulphate polyacrylamide gel electrophoresis) using Coomassie blue staining in the samples of the aerobic granule matrix formed in the presence of multivalent cations and compared with that of the aerobic granules cultivated without cations. The results indicate that the granule matrix is predominantly composed of large and complex proteins that are tightly bound within the granular structure. The tightly bound extracellular polymeric substances (TB-EPS) may play a role in improved mechanical stability of aerobic granules. In the supernatant fraction of the sludge, only a small amount of free proteins in the medium molecular mass range was detected. The protein with high molecular mass ( (cid:21) 116 kDa) produced in the reactors with added Ca 2 + . Ca 2 + had a considerable regulatory inﬂuence on production of extracellular proteins during aerobic granulation.
Evolution of Pharmacological Treatments and Associated Costs for Multiple Myeloma in the Public Healthcare System of Catalonia: A Retrospective Observational Study

Simple Summary In this study, we examined the prescription patterns and pharmaceutical costs for treatments of multiple myeloma (MM) in Catalonia’s public healthcare system over the past decade. MM is a complex and costly disease for which substantial pharmacological innovation has recently been introduced, and we aimed to understand how treatment choices and expenses have evolved. We analyzed data from 4556 MM patients and found that the number of treated patients increased annually. Drugs like lenalidomide and daratumumab showed significant utilization rises, impacting overall costs. Our findings shed light on the economic burden of MM treatment, emphasizing the importance of monitoring and optimizing healthcare resources allocation for better healthcare decision making. Abstract (1) Background: Our understanding of and treatment for multiple myeloma (MM) has advanced significantly, and new pharmacological treatments have promising benefits but high price tags. This study analyzes prescription patterns and pharmaceutical expenditure for MM treatments in Catalonia’s public healthcare system over eight years. (2) Methods: A retrospective observational study examined MM treatment data from 2015 to 2022 in Catalonia, using healthcare registries from the Catalan Health Service to collect information on patients, medicines used, and treatment costs. (3) Results: A total of 4556 MM patients received treatment, with a rising trend in the number of treated patients each year from 902 in 2015 to 1899 in 2022. The mean age was 68.9 years, and patients were almost evenly distributed by gender (51.5% male). Most patients were treated with bortezomib (3338 patients), lenalidomide (2952), and/or daratumumab (1093). Most drugs showed increased utilization annually, most significantly for lenalidomide and daratumumab. The total pharmacological treatment cost throughout the entire study period was EUR 321,811,249, with lenalidomide leading with the highest total cost (EUR 157,236,784), and daratumumab exhibiting the highest increase in annual expenditure. (5) Conclusions: The study reveals a progressive increase in the number of MM patients treated and rising pharmaceutical costs. Lenalidomide and daratumumab incurred the highest costs. The findings highlight MM treatment’s economic impact and the need to monitor prescription patterns and expenditures to optimize healthcare resources and decision making. Understanding these trends can guide resource allocation effectively.
Using Machine Learning to Predict Genes Underlying Differentiation of Multipartite and Unipartite Traits in Bacteria

Since the discovery of the second chromosome in the Rhodobacter sphaeroides 2.4. 1 by Suwanto and Kaplan in 1989 and the revelation of gene sequences, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. This phenomenon shattered the dogma of a unipartite genome (a single circular chromosome) in bacteria. Recently, Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL) have emerged as powerful tools in the investigation of big data in a plethora of disciplines to decipher complex patterns in these data, including the large-scale analysis and interpretation of genomic data. An important inquiry in bacteriology pertains to the genetic factors that underlie the structural evolution of multipartite and unipartite bacterial species. Towards this goal, here we have attempted to leverage machine learning as a means to identify the genetic factors that underlie the differentiation of, in general, bacteria with multipartite genomes and bacteria with unipartite genomes. In this study, deploying ML algorithms yielded two gene lists of interest: one that contains 46 discriminatory genes obtained following an assessment on all gene sets, and another that contains 35 discriminatory genes obtained based on an investigation of genes that are differentially present (or absent) in the genomes of the multipartite bacteria and their respective close relatives. Our study revealed a small pool of genes that discriminate bacteria with multipartite genomes and their close relatives with single-chromosome genomes. Machine learning thus aided in uncovering the genetic factors that underlie the differentiation of bacterial multipartite and unipartite traits.
Brain Iron Homeostasis and Mental Disorders

Iron plays an essential role in various physiological processes. A disruption in iron homeostasis can lead to severe consequences, including impaired neurodevelopment, neurodegenerative disorders, stroke, and cancer. Interestingly, the link between mental health disorders and iron homeostasis has not received significant attention. Therefore, our understanding of iron metabolism in the context of psychological diseases is incomplete. In this review, we aim to discuss the pathologies and potential mechanisms that relate to iron homeostasis in associated mental disorders. We propose the hypothesis that maintaining brain iron homeostasis can support neuronal physiological functions by impacting key enzymatic activities during neurotransmission, redox balance, and myelination. In conclusion, our review highlights the importance of investigating the relationship between trace element nutrition and the pathological process of mental disorders, focusing on iron. This nutritional perspective can offer valuable insights for the clinical treatment of mental disorders.
Symptomatic Dry Eye Disease and Associated Factors Among Adult Diabetic Patients in Adare General Hospital, Hawassa City, Southern Ethiopia, 2023

Introduction Symptomatic dry eye disease is a multifactorial ocular surface condition caused by disruption of the precorneal tear film and is a common clinical finding in diabetic patients. However, there was no study on the prevalence and associated factors of symptomatic dry eye disease among diabetic patients in Ethiopia or in the study area. Purpose This study aimed to investigate the prevalence and associated factors of symptomatic dry eye disease among adult diabetic patients in Adare General Hospital, Hawassa City, Southern Ethiopia, in 2023. Methods A hospital-based cross-sectional study design was conducted on 493 adult diabetic patients who were selected using systematic random sampling, from April 23 to June 8, 2023. Data were collected through a face-to-face interview using an ocular surface disease index questionnaire. Binary logistic regression was performed to identify factors potentially associated with symptomatic dry eye disease. Variable with a P value of <0.05 was considered statistically significant. Results A total of 488 subjects participated in this study with a response rate of 99%. The prevalence of symptomatic dry eye disease was 34.8% (95% CI = 30.6–39.1). College and university educational status (AOR = 5.88, 95% CI = 2.25–15.38), government employed (AOR = 2.22, 95% CI = 1.05–4.68), use of visual display unit >5 hours (AOR = 4.41, 95% CI = 1.51–12.87), duration of diabetes ≥11 years (AOR = 3.57, 95% CI = 1.28–9.90), poor glycemic control (AOR = 2.13, 95% CI = 1.21–3.75), allergic conjunctivitis (AOR = 1.99, 95% CI = 1.12–3.54), and debris in the tear film (AOR = 3.63, 95% CI = 1.53–8.61) were positively associated with symptomatic dry eye disease. Conclusion The study revealed a high prevalence of symptomatic dry eye disease. Higher educational status, government employed, use of visual display unit, longer duration of diabetes, poor glycemic control, allergic conjunctivitis, and tear film debris were significantly associated with symptomatic dry eye disease. Breaks in screen use, good glycemic control, and treatment of ocular morbidities such as allergic conjunctivitis and debris in the tear film were recommended for all diabetic patients.
Esculeogenin A, a Glycan from Tomato, Alleviates Nonalcoholic Fatty Liver Disease in Rats through Hypolipidemic, Antioxidant, and Anti-Inflammatory Effects

This study examined the preventative effects of esculeogenin A (ESGA), a newly discovered glycan from tomato, on liver damage and hepatic steatosis in high-fat-diet (HFD)-fed male rats. The animals were divided into six groups (each of eight rats): a control group fed a normal diet, control + ESGA (200 mg/kg), HFD, and HFD + ESAG in 3 doses (50, 100, and 200 mg/kg). Feeding and treatments were conducted for 12 weeks. Treatment with ESGA did not affect gains in the body or fat weight nor increases in fasting glucose, insulin, and HOMA-IR or serum levels of free fatty acids (FFAs), tumor-necrosis factor-α, and interleukin-6 (IL-6). On the contrary, it significantly reduced the serum levels of gamma-glutamyl transpeptidase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total triglycerides (TGs), cholesterol (CHOL), and low-density lipoprotein cholesterol (LDL-c) in the HFD-fed rats. In addition, it improved the liver structure, attenuating the increase in fat vacuoles; reduced levels of TGs and CHOL, and the mRNA levels of SREBP1 and acetyl CoA carboxylase (ACC); and upregulated the mRNA levels of proliferator-activated receptor α (PPARα) and carnitine palmitoyltransferase I (CPT I) in HFD-fed rats. These effects were concomitant with increases in the mRNA, cytoplasmic, and nuclear levels of nuclear factor erythroid 2-related factor 2 (Nrf2), glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and heme oxygenase-1 (HO); a reduction in the nuclear activity of nuclear factor-kappa beta (NF-κB); and inhibition of the activity of nuclear factor kappa B kinase subunit beta (IKKβ). All of these effects were dose-dependent effects in which a normal liver structure and normal levels of all measured parameters were seen in HFD + ESGA (200 mg/kg)-treated rats. In conclusion, ESGA prevents NAFLD in HFD-fed rats by attenuating hyperlipidemia, hepatic steatosis, oxidative stress, and inflammation by acting locally on Nrf2, NF-κB, SREBP1, and PPARα transcription factors.