Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Leucine-Rich Glioma-Inactivated 1 (Anti-LGI 1) Limbic Encephalitis and New-Onset Psychosis: A Case Report

Anti-leucine-rich glioma-inactivated 1 limbic encephalitis (Anti-LGI 1 LE) is a subtype of autoimmune encephalitis (AE) and the most common cause of limbic encephalitis (LE). Clinically, it can have an acute to sub-acute onset of confusion and cognitive impairment, facial-brachial dystonic seizures (FDBS), and psychiatric disturbances. The clinical manifestations are varied, and its diagnosis requires high clinical suspicion to avoid delay in the treatment. When patients manifest mostly psychiatric symptoms, the disease may not be immediately recognized. We aim to report a case of Anti-LGI 1 LE in which the patient presented acute psychotic symptoms and was initially diagnosed with unspecified psychosis. We present a case of a patient with sub-acute behavioral changes, short-term memory loss, and insomnia who was brought to the emergency department after a sudden episode of disorganized behavior and speech. On medical examination, the patient presented persecutory delusions and indirect signs of auditory hallucinations. An initial diagnosis of unspecified psychosis was performed. Investigations revealed right temporal epileptiform activity in the electroencephalogram (EEG), abnormal bilateral hyperintensities in the temporal lobes in the brain magnetic resonance imaging (MRI), and a positive titer of anti-LGI 1 antibodies (Abs) in serum and cerebrospinal fluid (CSF), leading to a diagnosis of anti-LGI 1 LE. The patient was treated with intravenous (IV) steroids and immunoglobulin and later with IV rituximab. In patients that predominantly present with psychotic and cognitive disorders the diagnosis of anti-LGI 1 LE can be delayed predisposing them to a poorer prognosis (permanent cognitive impairment - especially short-term memory loss - and persistent seizures). It is necessary to be aware of this diagnosis when evaluating acute to sub-acute psychiatric illness developing with cognitive impairment (specially with memory loss) to avoid diagnosis delays and long-term sequelae.
Multi–Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non–Contact Sensors

Fault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. The advancements in machine learning and deep learning have led to enhanced performance of classification. Two important elements of fault diagnosis using machine learning are data preprocessing and model structure. Multi–class classification is used to classify faults into different single types, whereas multi–label classification classifies faults into compound types. It is valuable to focus on the capability of detecting compound faults because multiple faults can exist simultaneously. Diagnosis of untrained compound faults is also a merit. In this study, input data were first preprocessed with short–time Fourier transform. Then, a model was built for classification of the state of the system based on multi–output classification. Finally, the proposed model was evaluated based on its performance and robustness for classification of compound faults. This study proposes an effective model based on multi–output classification, which can be trained using only single fault data for the classification of compound faults and confirms the robustness of the model to changes in unbalance.
NanoSIMS sulfur isotopic analysis at 100 nm scale by imaging technique

NanoSIMS has been widely used for in-situ sulfur isotopic analysis (32S and 34S) of micron-sized grains or complex zoning in sulfide in terrestrial and extraterrestrial samples. However, the conventional spot mode analysis is restricted by depth effects at the spatial resolution < 0.5–1 μm. Thus sufficient signal amount cannot be achieved due to limited analytical depths, resulting in low analytical precision (1.5‰). Here we report a new method that simultaneously improves spatial resolution and precision of sulfur isotopic analysis based on the NanoSIMS imaging mode. This method uses a long acquisition time (e.g., 3 h) for each analytical area to obtain sufficient signal amount, rastered with the Cs+ primary beam of ∼100 nm in diameter. Due to the high acquisition time, primary ion beam (FCP) intensity drifting and quasi-simultaneous arrival (QSA) significantly affects the sulfur isotopic measurement of secondary ion images. Therefore, the interpolation correction was used to eliminate the effect of FCP intensity variation, and the coefficients for the QSA correction were determined with sulfide isotopic standards. Then, the sulfur isotopic composition was acquired by the segmentation and calculation of the calibrated isotopic images. The optimal spatial resolution of ∼ 100 nm (Sampling volume of 5 nm × 1.5 μm2) for sulfur isotopic analysis can be implemented with an analytical precision of ∼1‰ (1SD). Our study demonstrates that imaging analysis is superior to spot-mode analysis in irregular analytical areas where relatively high spatial resolution and precision are required and may be widely applied to other isotopic analyses.
EEG-Based Emotion Recognition via Knowledge-Integrated Interpretable Method

Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and performance promotion in EEG-related tasks. In this work, we explore the use of representative interpretable models to analyze the learning behavior of convolutional neural networks (CNN) in EEG-based emotion recognition. According to the interpretable analysis, we find that similar features captured by our model and state-of-the-art model are consistent with previous brain science findings. Next, we propose a new model by integrating brain science knowledge with the interpretability analysis results in the learning process. Our knowledge-integrated model achieves better recognition accuracy on standard EEG-based recognition datasets.
Behavior of circular chains of nonlinear oscillators with Kuramoto-like local coupling

The conditions under which synchronization is achieved for a one-dimensional ring of identical phase oscillators with Kuramoto-like local coupling are studied. The system is approached in the weakly coupled approximation as phase units. Instead of global couplings, the nearest-neighbor interaction is assumed. Units are pairwise coupled by a Kuramoto term driven by their phase differences. The system exhibits a rich set of behaviors depending on the balance between the natural frequency of isolated units and the self-feedback. The case of two oscillators is solved analytically, while a numerical approach is used for N > 2. Building from Kuramoto, the approach to synchronization, when possible, is studied through a local complex order parameter. The system can eventually evolve as a set of coupled local communities toward a given phase value. However, the approach to the stationary state shows a non-monotonous non-trivial dynamic.
Object recognition based on improved YOLOv5

At presen, object recognition task is troubled by its huge kinds of objects. In this paper, the SIoU loss function and YOLOv5 deep learning convolutional neural network are innovatively used to improve the training efficiency and recognition accuracy. Unlike the traditional bounding box regression loss function (e.g. Giou, Diou[1] , CIoU) , which only focuses on the distance between the prediction box and the ground true box, the size of the overlap area, and one or more of the aspect ratios, and sets the impact factor on this basis, the SIoU loss function also introduces Angle cost to fit the best regression direction, which makes the direction of bounding box regression more reasonable and improves the regression efficiency[1]. In this paper, the defects of traditional loss function and the calculation method of SIoU loss function are introduced, and the performance between SIoU and CIoU is compared.
Mathematical methods for science students

This well-known and widely recommended textbook provides an essential basis of mathematical techniques for engineers, physicists, chemists and management scientists at undergraduate level. The material is suitable for the first two years of a typical University or Polytechnic course, and it is developed assuming only an elementary knowledge of pre-University mathematics. The text includes a large number of worked examples, and there is a selection of unworked problems at the end of each chapter.
Network-Based Method to Investigate the Promoted Cell Apoptosis Mechanisms of Oridonin in OSCC through the RNA-Transcriptome

The morbidity of oral cancer is high in the world. Oridonin is a traditional Chinese medicine that can effectively inhibit oral squamous cell carcinoma (OSCC) growth, but its mechanism remains unclear. Our previous data showed that oridonin inhibited CAL-27 cell proliferation and promoted apoptosis. Herein, we explored the mechanism and target of oridonin in human OSCC through RNA sequencing and integration of multiple bioinformatics analysis strategies. Differences in gene expression can be analyzed with RNA sequencing. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), gene set enrichment analysis (GSEA), Disease Ontology (DO), and other enrichment analyses were used to evaluate differentially expressed genes (DEGs). Protein–protein interaction (PPI) networks were built via the STRING database. It was found that tumor necrosis factor (TNF) signaling pathway, cytokine–cytokine receptor interaction, and nuclear factor-kappa B (NF-kappaB) signaling pathway were associated with the therapeutic effects of oridonin in OSCC. Three key genes (BIRC3, TNFSF10, and BCL6) were found to associate with cell apoptosis in OSCC cells treated with oridonin. Quantitative PCR assays verified the expression of apoptosis-related DEGs: TNFSF10, BIRC3, AIFM2, BCL6, BCL2L2, and Bax. Western blots were employed for verifying proteins expression associated with DEGs: cleaved caspase 3, Bax, Bcl-w, anti-cIAP2, and anti-TRAIL. In conclusion, our findings reveal the molecular pathways and targets by which oridonin can treat and induce cytotoxic effects in OSCC: by affecting the signaling including TNF, NF-κB, and cytokine-cytokine receptor interaction and by regulating the key gene BIRC3, TNFSF10, and BCL6. It should be noted that further clinical trial validation is very necessary. Combined with current research trends, our existing research may provide innovative research drugs for the treatment of OSCC.
Active Human-Following Control of an Exoskeleton Robot With Body Weight Support

This article presents an active human-following control of the lower limb exoskeleton for gait training. First, to improve safety, considering the human balance, the OpenPose-based visual feedback is used to estimate the individual’s pose, then, the active human-following algorithm is proposed for the exoskeleton robot to achieve the body weight support and active human-following. Second, taking the human’s intention and voluntary efforts into account, we develop a long short-term memory (LSTM) network to extract surface electromyography (sEMG) to build the estimation model of joints’ angles, that is, the multichannel sEMG signals can be correlated with flexion/extension (FE) joints’ angles of the human lower limb. Finally, to make the robot motion adapt to the locomotion of subjects under uncertain nonlinear dynamics, an adaptive control strategy is designed to drive the exoskeleton robot to track the desired locomotion trajectories stably. To verify the effectiveness of the proposed control framework, several recruited subjects participated in the experiments. Experimental results show that the proposed joints’ angles estimation model based on the LSTM network has a higher estimation accuracy and predicted performance compared with the existing deep neural network, and good simultaneous locomotion tracking performance is achieved by the designed control strategy, which indicates that the proposed control can assist subjects to perform gait training effectively.
Is it feasible and ethical to randomize patients between surgery and non-surgical treatments for gastrointestinal cancers?

Background In several settings in the treatment of gastrointestinal cancers, it is unclear if the addition of surgery to a multimodal treatment strategy, or in some circumstances its omission, lead to a better outcome for patients. In such situations of clinical equipoise, high-quality evidence from randomised-controlled trials is needed to decide which treatment approach is preferable. Objective In this article, we outline the importance of randomised trials comparing surgery with non-surgical therapies for specific scenarios in the treatment of gastrointestinal cancers. We explain the difficulties and solutions of designing these trials and recruiting patients in this context. Methods We performed a selective review based on a not systematic literature search in core databases, supplemented by browsing health information journals and citation searching. Only articles in English were selected. Based on this search, we discuss the results and methodological characteristics of several trials which randomised patients with gastrointestinal cancers between surgery and non-surgical treatments, highlighting their differences, advantages, and limitations. Results and conclusions Innovative and effective cancer treatment requires randomised trials, also comparing surgery and non-surgical treatments for defined scenarios in the treatment of gastrointestinal malignancies. Nevertheless, potential obstacles to designing and carrying out these trials must be recognised ahead of time to avoid problems before or during the trial.
Fucosylated TLR4 mediates communication between mutualist fucotrophic microbiota and mammalian gut mucosa

Objective The glycans on the mucosa of suckling mice are predominantly sialylated; upon weaning, fucosylated glycans preponderate. This manifestation of mutualism between fucotrophic bacteria and the mature host utilizes a sentinel receptor in the intestinal mucosa; this receptor was isolated to distinguish its structural and functional features. Design Provisional identification of the sentinel gut receptor as fuc-TLR4 was through colonization of germ-free mutant mice. Conventional mice whose microbiota was depleted with a cocktail of antibiotics were used to further define the nature and functions of fuc-TLR4 sentinel, and to define the role of the fucotrophic microbiota in gut homeostasis and recovery from insult. The nature of the sentinel was confirmed in cultured human HEL cells. Results Fuc-TLR4 activity is distinct from that of TLR4. Activated mucosal fuc-TLR4 induces a fuc-TLR4 dependent non-inflammatory (ERK and JNK dependent, NF-κB independent) signaling cascade, initiating induction of fucosyltransferase 2 (secretor) gene transcription. In vitro, either defucosylation or TLR4 knockdown abrogates FUT2 induction, indicating that fuc-TLR4 activity requires both the peptide and glycan moieties. In vivo, fucose-utilizing bacteria and fucose-binding ligands induce mucosal fucosylation. Activation of this pathway is essential for recovery from chemically induced mucosal injury in vivo. Conclusion In mature mice, fucosyl-TLR4 mediated gut fucosylation creates a niche that supports the healthy fucose-dependent mutualism between the mammalian gut and its fucotrophic microbes. Such microbiota-induced Fuc-TLR4 signaling supports initial colonization of the secretor gut, recovery from dysbiosis, and restoration or preservation of intestinal homeostasis.
A laser induced fluorescence instrument for measuring tropospheric NO2

Nitrogen dioxide (NO2), an essential gas in atmospheric chemistry, is almost always measured indirectly with just one technique: photolysis of NO2 to NO, followed by detection of resulting NO. However, the photolysis introduces artifact signals that complicate the extraction of the NO2 signal at low NO2 volume mixing ratios. A new prototype instrument uses laser induced fluorescence to measure tropospheric NO2 directly. The atmospheric fluorescence spectra measured with this instrument match the fluorescence spectra of NO2 in a reference cell over the wavelength range 565.1–565.2 nm. This match indicates that fluorescence from other chemical species is not interfering with the measurements. The prototype’s current detection limit is 280 parts per trillion (pptv) in 2.5 min with an absolute uncertainty of ±15% (2σ confidence). This detection limit, which can be improved with better optical filtering and more laser power, is currently adequate for urban air conditions.
Morphological characteristics and electrical conduction in syndiotactic polypropylene

Molecular structure, morphological characteristics and electrical conduction in syndiotactic polypropylene (sPP) are studied. sPP is found to be highly resistant to thermal oxidation. The crystallinity measured by the x-ray diffraction meter is very low and shows no significant change with annealing, compared with that in isotactic polypropylene (iPP). The development of spherulites occurs at a lower temperature than in iPP and the size of spherulites is much smaller than that in iPP, even though the number of spherulites increases drastically. The influence of the morphological change on electrical conduction in sPP is negligibly small, in contrast to iPP, where the breakdown strength decreases markedly due to spherulite growth upon heat treatment. In electrical conduction in the high field region above 107 V m-1 , the predominant conduction mechanism is interpreted to be the Schottky effect in the low temperature region below 70 °C. But in the high temperature region above 70 °C, the conduction is considered to be ionic conduction, with the jump distance estimated to be 3.3 nm smaller than that in iPP. These results are discussed in terms of the molecular structure differences between sPP and iPP.
Cuproplasia characterization in colon cancer assists to predict prognosis and immunotherapeutic response

Introduction Colon cancer is the 3rd most prevalent cancer worldwide, with more than 900,000 deaths annually. Chemotherapy, targeted treatment, and immunotherapeutic treatment are the three cornerstones of colon cancer treatment; however, the occurrence of immune therapy resistance is the most pressing problem to solve. Copper is a mineral nutrient that is both beneficial and potentially toxic to cells and is increasingly implicated in cell proliferation and death pathways. Cuproplasia is characterized by copper-dependent cell growth and proliferation. This term encompasses both neoplasia and hyperplasia and describes the primary and secondary effects of copper. The connection between copper and cancer has been noted for decades. However, the relationship between cuproplasia and colon cancer prognosis remains unclear. Method In this study, we applied bioinformatics approaches including WGCNA, GSEA and etc. to delineate cuproplasia characterization of colon cancer, set up a robust Cu_riskScore model based on cuproplasia-relevant genes and found its relevant biological processes use qRT-pCR to validate our results on our cohort. Result The Cu_riskScore is found to be relevant to Stage and MSI-H subtype, and some biological processes including MYOGENESIS and MYC TARGETS. The Cu_riskScore high and low groups also showed different immune infiltration pattern and genomic traits. Finally, the result of our cohort showed the Cu_riskScore gene RNF113A has a marked effect in predicting immunotherapy response. Discussion In conclusion, we identified a cuproplasia-related gene expression signature consisting of six genes and studied the landscape of the clinical and biological characterization of this model in Colon Cancer. Furthermore, the Cu_riskScore was demonstrated to be a robust prognostic indicator and predictive factor for the benefits of immunotherapy.
The Behaviour of Scattered Plane Waves of Elastic Wave Equations and Applications to Scattering Theory

We study singularities of scattered plane elastic waves by a convex obstacle. As an application we show a theorem on the position of singularities of the scattering kernel S(•,−ω,ω). From this theorem we can prove that the convexity of the obstacle is decided from the position of singularities of S(•,−ω,ω)
Chondro-Cutaneous Autograft for Reconstruction of the Nasal Ala After a Human Bite

Patient: Male, 58-year-old Final Diagnosis: Trauma Symptoms: Skin lesion Clinical Procedure: — Specialty: Plastic Surgery Objective: Rare disease Background: The nasal ala is a paired structural subunit of the nose that is functionally important in the maintenance of the nasal valve. It consists of 3 anatomically distinct layers: skin, cartilage, and mucosa, all of which need to be restored in reconstructive surgeries to maintain nasal patency. When multiple layers are involved in a defect, the reconstruction requires combining procedures to replace each layer. Case Report: We describe a peculiar case of a 58-year-old man with a full-thickness loss of substance of the right nasal ala due to a human bite. The patient came to our hospital after an altercation with another man who bit his nose off. He was initially seen at a smaller regional hospital that did not have a plastic surgery department and was soon after transferred to our facility due to the complexity of the case. To reduce the risk of infection, the patient was treated with a complete course of intravenous antibiotic therapy and the wound was medicated daily with antiseptic solutions. The loss of substance was reconstructed with a composed graft from the auricle concha and the melolabial flap. Conclusions: Defects of the nasal ala are challenging to reconstruct, given its complex 3-dimensional structure. The successful repair of these defects provides aesthetic symmetry and preserves nasal function. A wide variety of reconstructive options have been utilized in many nasal reconstruction cases and have been documented. The combination of a chondro-cutaneous graft from the auricular concha and a melolabial flap graft allowed a good result without local or systemic complications.
Modelling the effects of land–sea contrast on tropical cyclone precipitation under environmental vertical wind shear

The precipitation pattern of a landfalling tropical cyclone (TC) with and without a weak environmental vertical wind shear (VWS) is investigated using WRF/NCAR model simulations under idealized conditions. In the simulations without VWS, results show that for the outer band (r ∼ 100–300 km), the cold and dry air originating over smooth land is advected offshore, reduces the stability and develops a band of rainfall on the eastern side of the TC, while the rough land surface tends to trigger more rainfall to the west. For the inner core (r< 100 km), there is only small rainfall asymmetry when the land surface is smooth and dry, but with a rough land surface, the rainfall asymmetry becomes evident and generally stronger rainfall is found over the land areas to the west. Further experiments are performed to compare the effects from weak environmental VWS and land–sea contrast. It is found that the storm‐scale (within 400 km from TC centre) VWS changes continuously in direction and magnitude due to asymmetric diabatic heating and accompanying upper‐level winds. The rainfall pattern in the inner‐core region follows closely the storm‐scale VWS with a downshear‐left relationship regardless of the surface properties, while in the outer‐band region, rainfall distribution is first strongly affected by the surface roughness before landfall and by the environmental VWS afterwards. Therefore, an evolving rainfall–VWS (both environmental and due to storm‐scale dynamics) relationship during TC landfall results.