Miracle-dz/UniFAR-FTUs huggingface dataset:
eIF4E as a molecular wildcard in metazoans RNA metabolism

The evolutionary origin of eukaryotes spurred the transition from prokaryotic‐like translation to a more sophisticated, eukaryotic translation. During this process, successive gene duplication of a single, primordial eIF4E gene encoding the mRNA cap‐binding protein eukaryotic translation initiation factor 4E (eIF4E) gave rise to a plethora of paralog genes across eukaryotes that underwent further functional diversification in RNA metabolism. The ability to take different roles is due to eIF4E promiscuity in binding many partner proteins, rendering eIF4E a highly versatile and multifunctional player that functions as a molecular wildcard. Thus, in metazoans, eIF4E paralogs are involved in various processes, including messenger RNA (mRNA) processing, export, translation, storage, and decay. Moreover, some paralogs display differential expression in tissues and developmental stages and show variable biochemical properties. In this review, we discuss recent advances shedding light on the functional diversification of eIF4E in metazoans. We emphasise humans and two phylogenetically distant species which have become paradigms for studies on development, namely the fruit fly Drosophila melanogaster and the roundworm Caenorhabditis elegans.
The Level of Inflammatory Markers and Their Relationship with Fat Tissue Distribution in Children with Obesity and Type 2 Diabetes Mellitus

Objective: This study aimed to determine the changes in proinflammatory and anti-inflammatory markers in children aged 10-18, who were not diagnosed with type 2 diabetes mellitus, were obese/overweight, and children with type 2 diabetes mellitus. In addition, we aimed to investigate whether these markers were associated with clinical and laboratory parameters, subcutaneous adipose tissue, preperitoneal adipose tissue, visceral adipose tissue, and hepatosteatosis. Materials and Methods: Children between the ages of 10 and 18, obese/overweight, with type 2 diabetes mellitus, and with a normal body mass index were included. Fat tissue thickness was measured. Tumor necrosis factor-α, interleukin-1β, interleukin-6, interleukin-18, and interferon-γ as proinflammatory markers and transforming growth factor-β and interleukin-10 levels as anti-inflammatory markers were studied. Results: Twenty-eight (31.8%) controls, 44 (50%) obese/overweight, and 16 (18.2%) patients with type 2 diabetes mellitus were included in our study. Age, sex, and puberty were similar between the groups. In the type 2 diabetes mellitus group, the subcutaneous fat tissue thickness was higher than that in the obese group, and the preperitoneal and visceral fat tissue thicknesses were similar to those in the obese group. Proinflammatory markers and interleukin-10 levels were similar in the obese/overweight, type 2 diabetes mellitus, and control groups. Transforming growth factor-β levels were significantly lower in the type 2 diabetes mellitus group than in the control group (P = .039). Transforming growth factor-β levels and other laboratory variables did not differ significantly in the type 2 diabetes mellitus group. Conclusion: While there was no change in all markers in the obese/overweight group compared with the control group, proinflammatory markers in the type 2 diabetes mellitus group were similar to those in the obese/overweight and control groups, and transforming growth factor-β level, an anti-inflammatory marker, was lower in the type 2 diabetes mellitus group than in the control group.
Generalized Topology of a Hybrid Switched- Capacitor Multilevel Inverter for High- Frequency AC Power Distribution

The generalized topology of a hybrid switched-capacitor multilevel inverter (SCMLI) for high-frequency ac (HFAC) power distribution system is presented. By employing the series–parallel conversion technique, the proposed SCMLI can achieve self-balancing of capacitor voltages and voltage step-up with arbitrary numbers of SC cells. The output power quality is improved by the intermediate voltage levels provided by the bidirectional switched converter connected to the dc source string. Besides, several active switches can be replaced with diodes if the large phase difference between output current and voltage is not required. Since the voltage ripple magnitude of the SC units is inversely proportional to the fundamental output frequency, the proposed SCMLI is more preferable for HFac applications. This paper elucidates the working principle of the proposed inverter with the detailed circuit and switching states’ description under staircase modulation. The mathematical analyses associated with the capacitor voltage ripples, sizing, and power loss are also provided. A 19-level MLI with a voltage gain of three was realized by cooperating two SC units with a voltage source string consisting of three symmetric dc sources. The operation and performance of the proposed inverter have been validated by the simulation and experiment on the inverter. The results showed that the inverter performed better at HFac condition and the power quality was satisfactory with the 19-level staircase output modulated at the fundamental frequency.
Critical appraisal of clinical practice guidelines for depression in children and adolescents

Abstract Background: Depression as a clinically significant and growing public health issue is the third leading cause of disability. Adolescent-onset depression has been associated with psychiatric, anxiety disorders, suicidality, medical comorbidities, and an elevated risk of major depressive disorder episodes later in life. How to effectively identify, prevent, and treat depression has become one of the key points of discussion among medical institutions/departments. With the importance of depression being emphasized, countries/organizations around the world have developed guidelines for clinical practice related to depression for different groups of people to guide medical staff to implement scientific, effective, and standardized depression management. However, the quality of such guidelines is not yet clear, which is not conducive to the selection of medical staff and affects clinical application to a certain extent. This study aims to evaluate the rigor of the development of clinical practice guidelines (CPGs) for depression in children and adolescents and will identify, in these documents, the recommendations for depression in children and adolescents. Methods: Electronic databases and specific databases of CPGs will be searched. Study selection and data extraction will be performed independently by 2 reviewers. The AGREE II Instrument and RIGHT checklist will be used to assess the methodological quality and reporting quality of included CPGs about depression in children and adolescents. We will also analyze consistency and inconsistency of the recommendations in CPGs, including assessment, diagnosis, screening, treatment, and management. Bubble charts will be used to show the differences in methodological and reporting quality. Subgroup analysis will be conducted according to the result of evaluation. Excel and Endnote X9 will be used. Results: Using the search drafts of electronic databases, we included 6 CPGs. The results of our study will be published in a peer-reviewed journal. Conclusions: Our study will provide systematic evidence for existing CPGs for depression in children and adolescents and provide a guidance for CPGs users. Protocol Registration: INPLASY202080002.
The Great Imitator: A Case of Accidental Kratom Overdose

Kratom (Mitragyna speciosa) is an herb that is sold over the counter in both pill and liquid forms. It contains opioid and stimulant properties and is used for relaxation as well as for weaning off opioid addictions. While a few adverse effects of kratom have been already reported, mainly with concerns around its toxicity, very little is known about it. We report a case of a female in her 40s presenting with signs of hypoxia reversed with naloxone administration, initially suspected to be a case of opioid overdose. Upon becoming alert and oriented, the patient and her husband reported that she consumed a large amount of kratom bought from the local gas station, and he later noticed that her lips were turning blue and she was becoming increasingly altered. Her urine toxicology was noted to be negative for opioids or any other substance use. The patient survived this accidental overdose due to the quick action of her husband, who rushed her to the emergency department (ED) upon realizing she appeared altered and very ill. It is important for emergency medicine practitioners to be aware of kratom overdose as a possible item on the differential diagnosis. This paper focuses on kratom overdose presentation and treatment.
The persistence, degradation, and mobility of metolachlor in soil and the fate of metolachlor and atrazine in surface water, surface water/sediment, and surface water/aquatic plant systems

The aim ofourimrestigation was to evaluate the influence ofsofl depth, soil moisture, and herbicide concentration on the persistence and d^radadon of metolachlor in soil. Metolachlor was more persistent in subsur&ce soOs than surface soil regardless of soil moisture or herbidde concentration. Greater quantities ofbound residues and extractable degradation products were found in the sur&ce soOs as a result of the increased soil sorption, and miaobial activity associated with soil containing more organic matter. Saturated soil favored degradation and the formation of bound residues compared to unsaturated soil. A significantly smaller percent of the applied metolachlor was bound to the soil or degraded in the sofl of the 90-^g,'g treated soil compared to the 9-^g/g treated soil. The reduction in degradation in the subsur&ce soils and the 90-|ag/g treated soil suggests metolachlor residues that leach to subsur^ce soil or large concentrations of metolachlor have a greater potential to contaminate surface water and groundwater as a result of their increased perastence and mobility.
Pathways and challenges to reinventing forestry in Newfoundland

This paper outlines the forest policy and decision-making framework in Newfoundland, including the history of the forest industry, and changing tenure arrangements, legislation, social values, labour markets, and forest conditions. Notably, forest policy objectives have shifted from a narrow focus on wood fibre growth and extraction to policies embodying a more diverse suite of objectives, including ecosystem-based management and inclusive public participation. The central questions of this paper revolve around the responses of forest managers and decisionmakers to this dynamic context: What is the current forest policy framework, and how (and why) has this changed over time? How does forest management and decision-making align with forest policy, and what are implications of gaps? Who is making decisions about forests and forestry in Newfoundland, who is benefiting from these decisions, and who is excluded?
A gut-restricted glutamate carboxypeptidase II inhibitor reduces monocytic inflammation and improves preclinical colitis

There is an urgent need to develop therapeutics for inflammatory bowel disease (IBD) because up to 40% of patients with moderate-to-severe IBD are not adequately controlled with existing drugs. Glutamate carboxypeptidase II (GCPII) has emerged as a promising therapeutic target. This enzyme is minimally expressed in normal ileum and colon, but it is markedly up-regulated in biopsies from patients with IBD and preclinical colitis models. Here, we generated a class of GCPII inhibitors designed to be gut-restricted for oral administration, and we interrogated efficacy and mechanism using in vitro and in vivo models. The lead inhibitor, (S)-IBD3540, was potent (half maximal inhibitory concentration = 4 nanomolar), selective, gut-restricted (AUCcolon/plasma > 50 in mice with colitis), and efficacious in acute and chronic rodent colitis models. In dextran sulfate sodium–induced colitis, oral (S)-IBD3540 inhibited >75% of colon GCPII activity, dose-dependently improved gross and histologic disease, and markedly attenuated monocytic inflammation. In spontaneous colitis in interleukin-10 (IL-10) knockout mice, once-daily oral (S)-IBD3540 initiated after disease onset improved disease, normalized colon histology, and attenuated inflammation as evidenced by reduced fecal lipocalin 2 and colon pro-inflammatory cytokines/chemokines, including tumor necrosis factor–α and IL-17. Using primary human colon epithelial air-liquid interface monolayers to interrogate the mechanism, we further found that (S)-IBD3540 protected against submersion-induced oxidative stress injury by decreasing barrier permeability, normalizing tight junction protein expression, and reducing procaspase-3 activation. Together, this work demonstrated that local inhibition of dysregulated gastrointestinal GCPII using the gut-restricted, orally active, small-molecule (S)-IBD3540 is a promising approach for IBD treatment. Description Oral drug (S)-IBD3540 displays robust preclinical anti-colitis activity and has relevant actions in patient-derived colon epithelial monolayers. Editor’s summary Many patients with moderate-to-severe inflammatory bowel disease (IBD) do not have adequate disease control, and additional treatments are needed. Glutamate carboxypeptidase II (GCPII), which is up-regulated in the colons of patients with IBD and in mouse models of colitis, is a promising therapeutic target. Here, Peters and colleagues developed an orally administered, gut-restricted, small molecule inhibitor of GCPII by conjugating 2-PMPA to the secondary bile acid deoxycholic acid. This inhibitor, termed (S)-IBD3540, reduced monocytic inflammation in mouse models of acute and chronic colitis and protected against the loss of barrier integrity in primary human colon epithelial air-liquid interface monolayers. These findings support the further translation of (S)-IBD3540 for the treatment of IBD in patients. —Melissa Norton
Multi-Valued Connected Consensus: A New Perspective on Crusader Agreement and Adopt-Commit

Algorithms to solve fault-tolerant consensus in asynchronous systems often rely on primitives such as crusader agreement, adopt-commit, and graded broadcast, which provide weaker agreement properties than consensus. Although these primitives have a similar flavor, they have been defined and implemented separately in ad hoc ways. We propose a new problem called connected consensus that has as special cases crusader agreement, adopt-commit, and graded broadcast, and generalizes them to handle multi-valued inputs. The generalization is accomplished by relating the problem to approximate agreement on graphs. We present three algorithms for multi-valued connected consensus in asynchronous message-passing systems, one tolerating crash failures and two tolerating malicious (unauthenticated Byzantine) failures. We extend the definition of binding, a desirable property recently identified as supporting binary consensus algorithms that are correct against adaptive adversaries, to the multi-valued input case and show that all our algorithms satisfy the property. Our crash-resilient algorithm has failure-resilience and time complexity that we show are optimal. When restricted to the case of binary inputs, the algorithm has improved time complexity over prior algorithms. Our two algorithms for malicious failures trade off failure resilience and time complexity. The first algorithm has time complexity that we prove is optimal but worse failure-resilience, while the second has failure-resilience that we prove is optimal but worse time complexity. When restricted to the case of binary inputs, the time complexity (as well as resilience) of the second algorithm matches that of prior algorithms.
Classification of lung cancer subtypes on CT images with synthetic pathological priors

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the"gold standard"information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score.
GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity

Static meshes with texture maps have attracted considerable attention in both industrial manufacturing and academic research, leading to an urgent requirement for effective and robust objective quality evaluation. However, current model-based static mesh quality metrics have obvious limitations: most of them only consider geometry information, while color information is ignored, and they have strict constraints for the meshes' geometrical topology. Other metrics, such as image-based and point-based metrics, are easily influenced by the prepossessing algorithms, e.g., projection and sampling, hampering their ability to perform at their best. In this paper, we propose Geodesic Patch Similarity (GeodesicPSIM), a novel model-based metric to accurately predict human perception quality for static meshes. After selecting a group keypoints, 1-hop geodesic patches are constructed based on both the reference and distorted meshes cleaned by an effective mesh cleaning algorithm. A two-step patch cropping algorithm and a patch texture mapping module refine the size of 1-hop geodesic patches and build the relationship between the mesh geometry and color information, resulting in the generation of 1-hop textured geodesic patches. Three types of features are extracted to quantify the distortion: patch color smoothness, patch discrete mean curvature, and patch pixel color average and variance. To the best of our knowledge, GeodesicPSIM is the first model-based metric especially designed for static meshes with texture maps. GeodesicPSIM provides state-of-the-art performance in comparison with image-based, point-based, and video-based metrics on a newly created and challenging database. We also prove the robustness of GeodesicPSIM by introducing different settings of hyperparameters. Ablation studies also exhibit the effectiveness of three proposed features and the patch cropping algorithm.
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems

In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users to opportunistically utilize detected spectrum holes. To this end, we propose a multi-class classification problem for wideband spectrum sensing to detect vacant spectrum spots based on collected I/Q samples. To enhance the accuracy of the spectrum sensing module, the outputs from the multi-class classification by each individual UAV are fused at a server in the unmanned aircraft system traffic management (UTM) ecosystem. In the spectrum scheduling phase, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users (i.e., UAVs). To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base-station (BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary users channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices.
Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges

Ontologies are used in various domains, with RDF and OWL being prominent standards for ontology development. RDF is favored for its simplicity and flexibility, while OWL enables detailed domain knowledge representation. However, as ontologies grow larger and more expressive, reasoning complexity increases, and traditional reasoners struggle to perform efficiently. Despite optimization efforts, scalability remains an issue. Additionally, advancements in automated knowledge base construction have created large and expressive ontologies that are often noisy and inconsistent, posing further challenges for conventional reasoners. To address these challenges, researchers have explored neuro-symbolic approaches that combine neural networks' learning capabilities with symbolic systems' reasoning abilities. In this chapter,we provide an overview of the existing literature in the field of neuro-symbolic deductive reasoning supported by RDF(S), the description logics EL and ALC, and OWL 2 RL, discussing the techniques employed, the tasks they address, and other relevant efforts in this area.
M$^2$-3DLaneNet: Exploring Multi-Modal 3D Lane Detection

Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. Previous works mainly focused on using images for 3D lane detection, leading to inherent projection error and loss of geometry information. To address these issues, we explore the potential of leveraging LiDAR for 3D lane detection, either as a standalone method or in combination with existing monocular approaches. In this paper, we propose M$^2$-3DLaneNet to integrate complementary information from multiple sensors. Specifically, M$^2$-3DLaneNet lifts 2D features into 3D space by incorporating geometry information from LiDAR data through depth completion. Subsequently, the lifted 2D features are further enhanced with LiDAR features through cross-modality BEV fusion. Extensive experiments on the large-scale OpenLane dataset demonstrate the effectiveness of M$^2$-3DLaneNet, regardless of the range (75m or 100m).