Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Profiles Collection from Twitter Users with Depressive Symptoms

Mental illness is a pressing issue that needs urgent attention, as the number of people suffering from mental disorders continues to increase. Diagnosing mental health disorders can be challenging, and gathering information about a patient's medical history and symptoms is crucial for an accurate diagnosis. Self-disclosure on social media can provide valuable insights into whether users may be suffering from a mental illness. This paper proposes a method for automatically collecting data from social media users who disclosed their depression. The proposed approach yielded a 97% accuracy rate with a majority of 95%.
Phylogenomic branch length estimation using quartets

Abstract Motivation Branch lengths and topology of a species tree are essential in most downstream analyses, including estimation of diversification dates, characterization of selection, understanding adaptation, and comparative genomics. Modern phylogenomic analyses often use methods that account for the heterogeneity of evolutionary histories across the genome due to processes such as incomplete lineage sorting. However, these methods typically do not generate branch lengths in units that are usable by downstream applications, forcing phylogenomic analyses to resort to alternative shortcuts such as estimating branch lengths by concatenating gene alignments into a supermatrix. Yet, concatenation and other available approaches for estimating branch lengths fail to address heterogeneity across the genome. Results In this article, we derive expected values of gene tree branch lengths in substitution units under an extension of the multispecies coalescent (MSC) model that allows substitutions with varying rates across the species tree. We present CASTLES, a new technique for estimating branch lengths on the species tree from estimated gene trees that uses these expected values, and our study shows that CASTLES improves on the most accurate prior methods with respect to both speed and accuracy. Availability and implementation CASTLES is available at https://github.com/ytabatabaee/CASTLES.
Patient Perceptions of a Virtual Reality-Based System for Pulmonary Rehabilitation: A Qualitative Analysis

The objective of this study was to evaluate the attitudes, beliefs, and perspectives of patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD) while using a virtual reality (VR) system supporting a home-based pulmonary rehabilitation (PR) program. Patients with a history of COPD exacerbations were asked to use a VR app for home-based PR and then undergo semi-structured qualitative interviews to provide their feedback on using the VR app. The mean age of the patients was 72±9 years ranging between 55 and 84 years old. The qualitative data were analyzed using a deductive thematic analysis. Findings from this study indicated the high acceptability and usability of the VR-based system for engaging in a PR program. This study offers a thorough examination of patient perceptions while utilizing a VR-based technology to facilitate access to PR. Future development and deployment of a patient-centered VR-based system will consider patient insights and suggestions to support COPD self-management according to patient requirements, preferences, and expectations.
Serum TGF‐β as a predictive biomarker for severe disease and fatality of COVID‐19

For targeted intervention in coronavirus disease 2019 (COVID‐19), there is a high medical need for biomarkers that predict disease progression and severity in the first days after symptom onset. This study assessed the utility of early transforming growth factor β (TGF‐β) serum levels in COVID‐19 patients to predict disease severity, fatality, and response to dexamethasone therapy. Patients with severe COVID‐19 had significantly higher TGF‐β levels (416 pg/mL) as compared to patients with mild (165 pg/mL, p < 0.0001) or moderate COVID‐19 (241 pg/mL; p < 0.0001). Receiver operating characteristics area under the curve values were 0.92 (95% confidence interval [CI] 0.85–0.99, cut‐off: 255 pg/mL) for mild versus severe COVID‐19, and 0.83 (95% CI 0.65–1.0, cut‐off: 202 pg/mL) for moderate versus severe COVID‐19. Patients who died of severe COVID‐19 had significantly higher TGF‐β levels (453 pg/mL) as compared to convalescent patients (344 pg/mL), and TGF‐β levels predicted fatality (area under the curve: 0.75, 95% CI 0.53–0.96). TGF‐β was significantly reduced in severely ill patients treated with dexamethasone (301 pg/mL) as compared to untreated patients (416 pg/mL; p < 0.05). Early TGF‐β serum levels in COVID‐19 patients predict, with high accuracy, disease severity, and fatality. In addition, TGF‐β serves as a specific biomarker to assess response to dexamethasone treatment.
Laminar specificity of the auditory perceptual awareness negativity: A biophysical modeling study

How perception of sensory stimuli emerges from brain activity is a fundamental question of neuroscience. To date, two disparate lines of research have examined this question. On one hand, human neuroimaging studies have helped us understand the large-scale brain dynamics of perception. On the other hand, work in animal models (mice, typically) has led to fundamental insight into the micro-scale neural circuits underlying perception. However, translating such fundamental insight from animal models to humans has been challenging. Here, using biophysical modeling, we show that the auditory awareness negativity (AAN), an evoked response associated with perception of target sounds in noise, can be accounted for by synaptic input to the supragranular layers of auditory cortex (AC) that is present when target sounds are heard but absent when they are missed. This additional input likely arises from cortico-cortical feedback and/or non-lemniscal thalamic projections and targets the apical dendrites of layer-5 (L5) pyramidal neurons. In turn, this leads to increased local field potential activity, increased spiking activity in L5 pyramidal neurons, and the AAN. The results are consistent with current cellular models of conscious processing and help bridge the gap between the macro and micro levels of perception-related brain activity.
Mesenchymal stem cells: An efficient cell therapy for tendon repair (Review)

Tendon injury is a common disorder of the musculoskeletal system caused by overuse or trauma. With increasing incidence of tendon injuries, it is necessary to find an effective treatment. Mesenchymal stem cells (MSCs) are attracting attention because of their high proliferative and self-renewal capacity. These functions of MSCs show promise in treating a variety of diseases, including immune and musculoskeletal system disorder and cardiovascular disease, and show especially satisfactory effects in the treatment of tendon injury. First, since MSCs have multidirectional differentiation potential, they differentiate into specific cells after induction in vivo and in vitro. Furthermore, MSCs have paracrine functions and can secrete biologically active molecules and exosomes such as cytokines, growth factors and chemokines to promote tissue repair and regeneration. In tendon injury, MSCs promote tendon repair through four mechanisms: Decreasing inflammation and promoting neovascularization and cell proliferation and differentiation. They are also involved in extracellular matrix reorganization by promoting collagen production and transforming type III collagen fibers to type I collagen fibers. The present review summarized preclinical experiments with different sources of MSCs and their mechanisms in tendon repair, as well as the limitations of MSCs in current clinical applications and directions that need to be explored in the future.
Cell membrane biomimetic nanoparticles in drug delivery

Despite the efficiency of nanoparticle (NP) therapy, in vivo investigations have shown that it does not perform as well as in vitro. In this case, NP confronts many defensive hurdles once they enter the body. The delivery of NP to sick tissue is inhibited by these immune‐mediated clearance mechanisms. Hence, using a cell membrane to hide NP for active distribution offers up a new path for focused treatment. These NPs are better able to reach the disease's target location, leading to enhanced therapeutic efficacy. In this emerging class of drug delivery vehicles, the inherent relation between the NPs and the biological components obtained from the human body was utilized, which mimic the properties and activities of native cells. This new technology has shown the viability of using biomimicry to evade immune system‐provided biological barriers, with an emphasis on restricting clearance from the body before reaching its intended target. Furthermore, by providing signaling cues and transplanted biological components that favorably change the intrinsic immune response at the disease site, the NPs would be capable interacting with immune cells regarding the biomimetic method. Thus, we aimed to provide a current landscape and future trends of biomimetic NPs in drug delivery.
Safety, Tolerability, and Antitumor Activity of Zipalertinib Among Patients With Non–Small-Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Exon 20 Insertions

PURPOSE Although several agents targeting epidermal growth factor receptor (EGFR) exon 20 insertions (ex20ins) have recently been approved by the US Food and Drug Administration, toxicities related to the inhibition of wild-type (WT) EGFR are common with these agents and affect overall tolerability. Zipalertinib (CLN-081, TAS6417) is an oral EGFR tyrosine kinase inhibitor (TKI) with a novel pyrrolopyrimidine scaffold leading to enhanced selectivity for EGFR ex20ins-mutant versus WT EGFR with potent inhibition of cell growth in EGFR ex20ins-positive cell lines. METHODS This phase 1/2a study of zipalertinib enrolled patients with recurrent or metastatic EGFR ex20ins-mutant non–small-cell lung cancer (NSCLC) previously treated with platinum-based chemotherapy. RESULTS Seventy-three patients were treated with zipalertinib at dose levels including 30, 45, 65, 100, and 150 mg orally twice a day. Patients were predominantly female (56%), had a median age of 64 years, and were heavily pretreated (median previous systemic therapies 2, range 1-9). Thirty six percent of patients had received previous non-ex20ins EGFR TKIs and 3/73 (4.1%) patients received previous EGFR ex20ins TKIs. The most frequently reported treatment-related adverse events of any grade included rash (80%), paronychia (32%), diarrhea (30%), and fatigue (21%). No cases of grade 3 or higher drug-related rash or diarrhea were observed at 100 mg twice a day or below. Objective responses occurred across all zipalertinib dose levels tested, with confirmed partial response (PR) observed in 28/73 (38.4%) response-evaluable patients. Confirmed PRs were seen in 16/39 (41%) response-evaluable patients at the dose of 100 mg twice a day. CONCLUSION Zipalertinib has encouraging preliminary antitumor activity in heavily pretreated patients with EGFR ex20ins-mutant NSCLC, with an acceptable safety profile, including low frequency of high-grade diarrhea and rash.
TRATAMENTO FARMACOLÓGICO REVERTE REMODELAMENTO ULTRAESTRUTURAL PANCREÁTICO ADVERSO E RESISTÊNCIA À INSULINA EM CAMUNDONGOS C57BL/6 ALIMENTADOS COM DIETA HIPERLIPÍDICA

O pâncreas sofre remodelamento estrutural em animais submetidos a sobrecarga alimentar hiperlipidica. Objetivo: Avaliar os efeitos de monoterapias e associacoes medicamentosas sobre o metabolismo de carboidratos e ultraestrutura pancreatica num modelo experimental de sindrome metabolica (SM). Metodos: Camundongos C57BL/ 6 de 3 meses de idade receberam dieta hiperlipidica (60% lipidios) ou dieta padrao (10% lipidios) por 10 semanas, sendo, entao, iniciado tratamento farmacologico, oito grupos experimentais foram formados: SC (dieta padrao, sem tratamento); HF (dieta hiperlipidica, sem tratamento); HF-T (HF+Telmisartana, 9mg%); HF-S (HF+Sitagliptina, 1,1g%); HF-M (HF+Metformina, 600 mg%) e as associacoes HF-TM, HF-TS e HF-SM. O tratamento durou 6 semanas. A presenca de esteatose pancreatica foi avaliada por microscopia eletronica de transmissao e a insulinemia por radioimunoensaio. Os indices Homa-IR e Homa-B foram tambem calculados. Resultados: A dieta HF resultou em sobrepeso nos animais apos duas semanas de administracao quando comparado ao grupo SC, condicao que foi mantida ao longo de todo o experimento (p<0,0001). Os indices Homa-IR (avalia a resistencia a insulina) e HOMA-B (avalia a hipersecrecao das ilhotas) encontraram-se aumentados no grupo HF (p<0,0001). Todos os tratamentos resultaram em normalizacao da insulinemia e reducao da resistencia a insulina. A microscopia eletronica revelou citoarquitetura preservada e ausencia de esteatose nos grupos tratados, enquanto o grupo HF apresentou celulas desorganizadas e goticulas de gordura no interior dos acinos. Conclusao: Os tratamentos propostos foram eficazes ao tratar a resistencia a insulina, promovendo insulinemia normal e reducao da esteatose pancreatica num modelo experimental de SM. Apoio: CNPq, Faperj.
Using imlifidase to elucidate the characteristics and importance of anti-GBM antibodies produced after start of treatment

ABSTRACT Background Autoantibodies are common in glomerulonephritis, but the clinical benefit of rapid elimination has not been determined, even in anti-glomerular basement membrane (GBM) disease. Even less is known about the importance of autoantibody characteristics, including epitope specificity and immunoglobulin G (IgG) subclass distribution. We aimed to address this by characterizing the autoantibody profile in anti-GBM patients: we utilized samples from the GOOD-IDES-01 (treating GOODpasture's disease with Imunoglobulin G Degrading Enzyme of Streptococcus pyogenous) (ClinicalTrials.gov identifier: NCT03157037) trial , where imlifidase, which cleaves all IgG in vivo within hours, was given to 15 anti-GBM patients. Methods In the GOOD-IDES-01 trial, plasmapheresis was (re)started if anti-GBM antibodies rebounded. Serum samples were collected prospectively for 6 months and analyzed for anti-GBM epitope specificity using recombinant constructs of the EA and EB epitopes, IgG subclass using monoclonal antibodies, and anti-neutrophil cytoplasmic antibodies (ANCA). The results were correlated with clinical data. Results Patients with a rebound (n = 10) tended to have lower eGFR at 6 months (11 vs 34 mL/min/1.73 m2, P = .055), and patients with dialysis at 6 months had a higher EB/EA ratio at rebound (0.8 vs 0.5, P = .047). Moreover, two patients demonstrated increasing epitope restriction and several patients displayed a shift in subclass distribution at rebound. Six patients were double positive for ANCA. ANCA rebound was seen in 50% of patients; only one patient remained ANCA positive at 6 months. Conclusions In this study, rebound of anti-GBM antibodies, especially if directed against the EB epitope, was associated with a worse outcome. This supports the notion that all means should be used to eliminate anti-GBM antibodies. In this study ANCA was removed early and long-term by imlifidase and cyclophosphamide.
Array controller system with cryogenic pre-amplifiers for MIMIZUKU

MIMIZUKU is a mid-infrared imager and spectrograph being developed for the University of Tokyo Atacama Observatory (TAO) 6.5-m telescope (PI: Y. Yoshii). To fully utilize a high atmospheric transmission of the Chajnantor site, MIMIZUKU covers a wide wavelength range from 2 to 38 μm with three array detectors: a HAWAII-1RG HgCdTe 1024 × 1024 array with a 5 μm cutoff manufactured by Teledyne, an Aquarius Si:As IBC 1024 × 1024 array by Raytheon, and a MF-128 Si:Sb BIB 128 × 128 array by DRS. We have newly developed an array controller system to operate these multiple arrays. A sampling rate higher than 0.5 MHz is required to prevent from saturation of their wells in broad-band imaging observations with MIMIZUKU due to high thermal background flux. Such high speed signals are dulled when passing through lines from the arrays to readout circuits. To overcome this problem, we have developed high-speed cryogenic buffer pre-amplifier circuits with commercial GaAs MESFETs, instead of Si JFETs, which are generally used in buffer amplifiers at cryogenic temperatures. The cryogenic buffer circuits are installed on an outer wall of the optical bench of MIMIZUKU at 20 K. We have measured readout noises of the array controller system including the cryogenic buffers in a test cryostat and room temperature circuits and confirmed that input referred noises of the system are lower than the specification value of the readout noise of the Aquarius array.
Results of the airlift challenge: a multi-agent AI planning competition

Planning the delivery of cargo as part of an airlift operation is a notoriously complex problem. Transportation routes can suddenly become inaccessible due to poor weather or other unexpected occurrences. Factors such as airplane speed, carrying capacity, and airport maximum-on-ground must also be considered to ensure on-time and efficient delivery. Unforeseen cargo needs may require quick re-planning to meet tight deadlines. To address the problem, we held the Airlift Challenge, an online multi-agent planning competition which concluded in early 2023. Competitors in the Airlift Challenge created innovative algorithms to execute a simulated airlift operation. The algorithms were scored against a set of increasingly complex evaluation scenarios while contending with unexpected events and disruptions. In this paper, we describe the competition and simulation environment, summarize results, and include write-ups of the top approaches provided by the winning teams.
Action clusters: an NLP-powered approach to detecting similarities of actions within battles

Battles and simulated battles are typically analyzed using standard statistics, e.g. the number of units damaged, the duration of combat, or the adherence to battle plan. Higher-level descriptors, however, are needed to truly understand and describe a battle. For example, these standard statistics are incapable of detecting or describing collaboration. Our work implements an NLP-inspired algorithm which analyzes battle actions and detects higher-level similarity among battle actions. Additionally, our work suggests possible applications of this technique.
Data-driven fault detection of rotating machinery using synthetic oversampling and generative adversarial network

In the fault detection of mechanical rotating parts such as gears and bearings using data-driven methods, the working condition data sets detected by sensors have the characteristic of imbalanced proportion of positive and negative samples, which makes it difficult for machine learning classification methods to accurately identify fault samples. This paper proposes a method combining Synthetic Minority Over-Sampling Technique (SMOTE) with Generative Adversarial Networks (SMOTIFIED-GAN) to pre-process the training data. The input random noise of generator in GAN is replaced by the synthetic sample of SMOTE, which makes the samples more consistent with the real distribution, to solve the imbalanced class problem, thus improve the fault diagnosis ability of the classification model. This method is applied to experimental gearbox fault datasets. The classification ability and robustness of the detection model in different imbalance rates are analysed, and the results show that SMOTIFIED-GAN improved the detection rate. Compared with the traditional methods, the F1 score of fault detection results is improved, and the feasibility of the proposed method.
High-sensitivity distributed pipelines infrastructure monitoring with internal deployed fibers and Rayleigh enhancement

Pipeline infrastructure monitoring based on distributed fiber-optic acoustic sensing is gaining significant attention aimed at real-time rapid detection of leakages, third-party intrusion, geo-hazards, corrosion, and other structural damages. Typical fibers installations are external to a pipeline, however retrofitting of existing pipelines through internal installation is desirable despite deployment challenges. Highly sensitive distributed acoustic sensing integrated within new pipelines or retrofit in existing pipelines can enable early detection of damage and degradation. In this work, we demonstrate pipeline integrity monitoring using distributed acoustic sensing and the Rayleigh backscattering-enhanced optical fibers deployed internal to the pipeline for high sensitivity detection of acoustic events. More specifically, traditional and backscattering-enhanced optical fibers are interrogated using bench-top phase-sensitive optical time-domain reflectometry (Φ-OTDR). The distributed acoustic sensing characteristics of two types of backscattered-enhanced fibers, Type A and Type B, are experimentally investigated. Our measurement analysis shows that the SNR of the acoustic event detection enhances ~2-fold and ~3-fold using the Type A and Type B fiber, respectively than that of the traditional SMF for pipeline monitoring. The presented investigation is a first validation for in-pipe deployed distributed acoustic sensing with high SNR and provides useful insight for diverse pipeline monitoring applications in the oil and gas distribution industry.
Face recognition algorithm based on sample expansion and label embedding for multi-resolution dictionary learning

Abstract. Face recognition, being a widely utilized and profoundly challenging task within the field of computer vision, has witnessed the emergence of numerous practical methodologies in recent years. Among these, dictionary learning-based methods have garnered extensive attention and have found widespread application. Nevertheless, it is worth noting that the majority of dictionary learning methods suffer from certain limitations. On the one hand, their primary focus lies solely on the resolution of the original images, thereby rendering them susceptible to the impact of resolution changes encountered in real-world scenarios. On the other hand, their ability to acquire a robust dictionary is hindered by the inadequacy of available training samples. We propose a multi-resolution dictionary learning method based on sample extension and label embedding. We generate virtual samples for the original training samples and convert all samples into different resolutions. In addition, noise constraints are applied to the virtual samples to enhance robustness. Then, we construct the label embedding term using the label information of the atoms and embed it on a multi-resolution dictionary learning model for training. The recognition rates of the method on the ORL, extended Yale B, CMU PIE, and GT datasets are 93.72%, 92.57%, 97.79%, and 73.91%, respectively.