Investigation of half metallic properties of Tl2Mo(Cl/Br)6 double perovskites for spintronic devices.

The manipulation of electronic device characteristics through electron spin represents a burgeoning frontier in technological advancement. Investigation of magnetic and transport attributes of the Tl2Mo(Cl/Br)6 double perovskite was performed using Wien2k and BoltzTraP code. When the energy states between ferromagnetic and antiferromagnetic conditions are compared, it is evident that the ferromagnetic state exhibits lower energy levels. Overcoming stability challenges within the ferromagnetic state is achieved through the manipulation of negative ΔHf within the cubic state. The analysis of the half metallicity character involves an analysis of band structure (BS) and DOS, elucidating its mechanism through PDOS using double exchange model p-d hybridization. The verification of 100% spin polarization is confirmed through factors such as spin polarization and the integer value of the total magnetic moment. Furthermore, the thermoelectric response, as indicated by the ratios of thermal-electrical conductivity and ZT, underscores the promising applications of these compounds in thermoelectric device applications.

Estimation of the economic impact of a bluetongue serotype 4 outbreak in Tunisia.

Introduction: Since 1999, Tunisia has experienced multiple occurrences of Bluetongue (BT) outbreaks, leading to numerous reported cases of infection and mortality in flocks. The re-emergence of the disease in 2020 caused substantial economic losses in cattle, attributed to the incursion of serotype BTV-4. Methods: To evaluate the economic impact of the recent BT episode, we conducted a retrospective study on outbreaks that occurred in Tunisia between August and November 2020, focusing on the impact at the owner's level and its effects on both small ruminants and cattle. A total of 234 ruminant farms (sheep, cattle, and mixed) were randomly selected across Tunisian governorates and included in the study to estimate both the direct and indirect costs of these outbreaks. Results: Total costs were calculated as the sum of losses and expenditures resulting from the BT outbreaks. At the animal level, total losses were estimated to range between 116.280 and 207.086 TND for one infected ewe (€33.721 and 60.055). For one lactating cow, costs varied between 2,590.724 and 3,171.107 TND (€751.310 and 919.621). In cattle, exposure to BTV led to a daily unit milk yield decrease of 12.50 to 14.66 L over an average period of 5 months. Diseased sheep experienced weight loss ranging between 4 and 10 kg during the BT outbreaks. The total mean cost of the 2020 BT outbreak in Tunisian investigated farms was estimated at 1,935 million TND (million €561.15) (range: 1,489 and 2,474 million TND; 431.81 and million €717.46). The most influential costs of the total BT outbreaks were the decrease in milk yield, mortality, and veterinary treatment. Discussion: This study gives valuable insights on the economic impact of the incursion of a new serotype of BT in a naive population in Tunisia. Considering the substantial costs incurred, it is imperative that this disease receives increased attention from stakeholders, including animal owners, veterinary services, practitioners, and decision-makers.

Topical Nitric Acid Burns: Initial Assessment and Management.

Nitric acid (NA) is corrosive. On contact with the skin, liquid splashes with nitric acid and may produce severe burns. These burns usually take on a characteristic yellowish hue. We report the case of a 54-year-old man presenting a nitric acid burn with a pathognomonic skin lesion and perfect healing. NA is a chemical that is important in industry, and it is a very strong acid that is used for engraving, metal refining, electroplating, and fertilizer manufacturing. Skin contact with NA leads to severe burns. The pathophysiology depends on the type of concentration, the strength, quality, and duration of contact, and the penetration power of the acids concerned. The early and abundant irrigation with water or sterile isotonic saline solution, the use of panthenol-containing creams and covering with silver sulphadiazine dressing, carefully monitoring wounds, keeping wounds clean and moist, and preventing and managing secondary infection allow the healing.

Two Cases of Biphasic Synovial Sarcoma With Expression of PAX8 and ER: A Diagnostic Pitfall.

Synovial sarcoma (SS) is a high-grade malignant neoplasm frequently arising in the deep soft tissue of the lower and upper extremities of young adults. Primary SS in the pelvis is extremely rare with scattered case reports. It often causes a diagnostic challenge in small biopsy and/or with aberrant expression of immunohistochemical markers. Here, we report 2 unusual cases of SS in the pelvis. Microscopically both cases present with biphasic morphology including spindle and epithelioid cells. In addition, the tumor cells in both cases expressed PAX8 and estrogen receptor. PAX8 is a transcription factor usually expressed in tumors of thyroid gland, kidney, and Müllerian system origin. The expression of PAX8 especially with co-expression of estrogen receptor can be misleading and result in a diagnosis of Müllerian tumors in female patients with pelvic masses. The diagnosis of SS for both cases was confirmed either with the fluorescence in situ hybridization or reverse transcription polymerase chain reaction showing a SS18 (SYT) (18q11) gene rearrangement. It is imperative to include SS in the differential diagnosis for malignant neoplasms exhibiting monotonous spindle cells (monophasic SS) and biphasic mixed monotonous spindle and epithelioid tumor cells in female patients with a pelvic mass. Molecular study for SS18 translocation is essential for the diagnosis in such cases.

A Rare Case of Cerebral Fat Embolism With No Respiratory or Dermatologic Involvement.

Fat embolism syndrome is potentially lethal. It is frequently a complication of long bone fractures and/or orthopedic surgery. Cerebral fat embolism is an unusual condition characterized by purely cerebral involvement. Neurological signs can be variable and brain MRI has a pivotal role in the diagnosis. We report the case of a 69-year-old male who presented motor impairment and a disorder of consciousness in the early postoperative course of total hip arthroplasty for a left femoral neck fracture, which occurred 24 hours before surgery. He had no dermatologic or respiratory signs. No respiratory or dermatologic signs were found. Blood samples showed moderate thrombopenia and hemolytic anemia. Multiple lesions were found on brain MRI. Diagnosis of cerebral fat embolism was established after ruling out differentials.

Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework.

The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult. To assess the reproducibility of previously published results, we re-implemented and evaluated 21 models in the PyKEEN software package. In this paper, we outline which results could be reproduced with their reported hyper-parameters, which could only be reproduced with alternate hyper-parameters, and which could not be reproduced at all, as well as provide insight as to why this might be the case. We then performed a large-scale benchmarking on four datasets with several thousands of experiments and 24,804 GPU hours of computation time. We present insights gained as to best practices, best configurations for each model, and where improvements could be made over previously published best configurations. Our results highlight that the combination of model architecture, training approach, loss function, and the explicit modeling of inverse relations is crucial for a model's performance and is not only determined by its architecture. We provide evidence that several architectures can obtain results competitive to the state of the art when configured carefully. We have made all code, experimental configurations, results, and analyses available at https://github.com/pykeen/pykeen and https://github.com/pykeen/benchmarking.

CLEP: a hybrid data- and knowledge-driven framework for generating patient representations.

SUMMARY: As machine learning and artificial intelligence increasingly attain a larger number of applications in the biomedical domain, at their core, their utility depends on the data used to train them. Due to the complexity and high dimensionality of biomedical data, there is a need for approaches that combine prior knowledge around known biological interactions with patient data. Here, we present CLinical Embedding of Patients (CLEP), a novel approach that generates new patient representations by leveraging both prior knowledge and patient-level data. First, given a patient-level dataset and a knowledge graph containing relations across features that can be mapped to the dataset, CLEP incorporates patients into the knowledge graph as new nodes connected to their most characteristic features. Next, CLEP employs knowledge graph embedding models to generate new patient representations that can ultimately be used for a variety of downstream tasks, ranging from clustering to classification. We demonstrate how using new patient representations generated by CLEP significantly improves performance in classifying between patients and healthy controls for a variety of machine learning models, as compared to the use of the original transcriptomics data. Furthermore, we also show how incorporating patients into a knowledge graph can foster the interpretation and identification of biological features characteristic of a specific disease or patient subgroup. Finally, we released CLEP as an open source Python package together with examples and documentation. AVAILABILITY AND IMPLEMENTATION: CLEP is available to the bioinformatics community as an open source Python package at https://github.com/hybrid-kg/clep under the Apache 2.0 License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.

Knowledge of the molecular interactions of biological and chemical entities and their involvement in biological processes or clinical phenotypes is important for data interpretation. Unfortunately, this knowledge is mostly embedded in the literature in such a way that it is unavailable for automated data analysis procedures. Biological expression language (BEL) is a syntax representation allowing for the structured representation of a broad range of biological relationships. It is used in various situations to extract such knowledge and transform it into BEL networks. To support the tedious and time-intensive extraction work of curators with automated methods, we developed the BEL track within the framework of BioCreative Challenges. Within the BEL track, we provide training data and an evaluation environment to encourage the text mining community to tackle the automatic extraction of complex BEL relationships. In 2017 BioCreative VI, the 2015 BEL track was repeated with new test data. Although only minor improvements in text snippet retrieval for given statements were achieved during this second BEL task iteration, a significant increase of BEL statement extraction performance from provided sentences could be seen. The best performing system reached a 32% F-score for the extraction of complete BEL statements and with the given named entities this increased to 49%. This time, besides rule-based systems, new methods involving hierarchical sequence labeling and neural networks were applied for BEL statement extraction.

BioKEEN: a library for learning and evaluating biological knowledge graph embeddings.

SUMMARY: Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs' nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies. AVAILABILITY AND IMPLEMENTATION: BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Magnetic properties of magnetically soft nanocomposite Co-SiO2 prepared via mechanical milling.

Nanocomposite of Co-SiO2, a soft magnetic material, with Co weight fraction x = 0.3 and 0.7 was prepared via mechanical milling. The magnetic properties of these samples, both zero-field-cooled (ZFC) and field-cooled (FC), have been measured as a function of x, milling time, and temperature. The structural assessment of the composite indicates a presence of only ferromagnetic (FM) hcp-Co phase in the composite. However, reported magnetic properties of these composites appear to be dependent on the presence of antiferromagnetic (AFM) phases of cobalt oxide as well. The observed enhancement in ZFC coercivity and a reduction in saturation magnetization with the milling time are due to an increase in defect density upon milling. The ZFC coercivity for the x = 0.3 samples has been found to be much higher than the x = 0.7 samples for all milling times. The coercivity above 50 K depends on temperature according to the law corresponding to isotropic uniaxial superparamagnetic particles. Below 50 K the presence of an AFM phase Co3O4 (TN approximately 33 K) and increased interparticle interactions bring in a departure from that law. The saturation magnetization is found to be temperature dependent for the x = 0.3 samples and temperature independent for the x = 0.7 samples, which further provides evidence of the presence of higher AFM phase fraction in the composite with a low metal volume fraction. The FC magnetic measurements show a presence of an exchange bias field and an enhanced coercivity which are higher than the ZFC measurements. All magnetic measurements indicate that the overall magnetic properties of the composite are dictated by the presence of a trace amount of cobalt oxides.