Miracle-dz/UniFAR-FTUs huggingface dataset:
Anxiety and Perception of Disease Control in Multiple Sclerosis Subjects Treated with Natalizumab

Multiple sclerosis subjects treated with natalizumab face anxiety about developing progressive multifocal leukoencephalopathy (PML), besides the psychological distress caused by the disease. The aim of this study is to investigate whether increasing the frequency of neurological and nuclear magnetic resonance screening may affect anxiety and the perception of disease control in patients treated with natalizumab. A total of 62 relapsing–remitting multiple sclerosis patients were recruited from 2019 to 2020. All patients received conventional infusion treatments with natalizumab, along with a screening protocol for PML. Three clinical assessments were considered: at the beginning of the study (T0), after 3 months (T1) and after 6 months (T2). Patients were classified into three levels of risk, where level 1 represented a low risk of PML and level 3 a high risk. This classification determined treatment and screening protocol, i.e., the frequency of performing the Stratify test and the brain 3T NMR exam, as well as the frequency of infusion treatments. Anxiety and perception of disease control were assessed at T0, T1, and T2 by a skilled psychologist. The Friedman test and the Wilcoxon signed-rank test were used to compare outcomes at baseline with the two follow-ups. Statistical test results showed that the risk of PML (per 1000 patients) was significantly lower in women than in men (W = 198.5; p = 0.01). Moreover, significant differences between baseline and the two follow-ups were found, both for anxiety (F(2) = 122.6, p < 0.001) and for perception of disease control (F(2) = 123.5, p < 0.001). In both cases, there was significant improvement between baseline (T0) and the end of the study (T2) in any risk level (p < 0.001). An increase in the number of follow-ups, as well as an increase in instrumental investigations, might have a positive effect on both anxiety and the perception of disease control. However, there are many variables involved in the disease process that have an impact on patients’ psychological well-being. Therefore, further and more extensive studies are necessary to evaluate how, and how much, each variable impacts the disease course.
Joint Constrained Learning for Causal Event-Event Relation Extraction of Brain Connectome

Brain science research has entered the era of con-nectome. The era of connectome has brought new challenges and opportunities for brain science research. Many studies have reported the structural and functional connections of the brain, but extracting scientific evidences from them is not easy. Traditional neuroimaging text mining methods based on terms are not suitable for the complex experimental designs and results analysis of brain connectome studies. Therefore, this paper proposes a novel method for event-level neuroimaging text mining, which aims to extract causal event-event relations of brain connectome. The method uses a deep learning model that combines BiLSTM and MLP, and incorporates constraints learning to enhance the model's performance on few-shot datasets. The constraints include common sense constraints and domain constraints, which help the model to learn from prior knowledge and domain expertise. The experiments on a brain connectome article dataset show that the proposed method can effectively extract the causal event-event relations of brain connectome with low resource requirements.
A Streaming Application for Real-Time Analytics of Seismic Data

An earthquake is shaking of the surface of the Earth resulting from a sudden release of energy in the Earth’s lithosphere. A few massive, irregularly shaped slabs of rock known as tectonic plates are present inside the Earth which are always under a slow and continuous motion. However, in some cases they might get stuck at their edges as a result of friction. When the stress on the edges of the tectonic plates overcomes the friction, it causes an earthquake which releases energy due to which shaking is felt during an earthquake. There are not many well-defined prediction methods. However, usually a long-term analysis of the earthquakes in a specific location is used for predicting an earthquake. The proposed work is to model a real-time streaming application using Apache Kafka, Apache Spark, MongoDB, and Dash that can help in the prediction and real-time analytics of the seismic data. The seismic data of thirty days was obtained from the IRIS Seismic Monitor. This data was used to develop a real-time streaming application with a real-time dashboard.
Factors Influencing Glucose Response to Exercise Following Oral Glucose Load

PURPOSE: Exercise can reduce postprandial hyperglycemia by increasing contraction-mediated glucose uptake in skeletal muscles. However, postprandial exercise does not always have similar effects in all patients. This study aimed to analyze the factors influencing glucose response to exercise following oral glucose load.METHODS: We enrolled 169 adults with impaired glucose tolerance (IGT, n = 64) and type 2 diabetes (T2D, n = 105). We measured fingertip capillary blood glucose level at 30-minute intervals over 2-hour after the 75-g oral glucose ingestion. In addition, a single bout of moderate-intensity aerobic exercise was conducted at 30 minutes after 75-g oral glucose ingestion. We also measured glycated hemoglobin (HbA1c), insulin, insulin resistance (HOMA-IR), β-cell function (HOMA-β), physical activity level questionnaire and anthropometrics.RESULTS: There was a significant difference in glucose concentration between the IGT and T2D groups (p <.001). Additionally, we found that patients with short diabetes duration (≤1 year) had significantly more decrease in glucose concentration than long diabetes duration (≥10 years) (p <.001). A regression analysis showed that waist circumference, visceral fat area, fasting glucose, HbA1c, and HOMA-IR affected glucose response to exercise following oral glucose load in patients with T2D.CONCLUSIONS: Higher volumes of exercise following an oral glucose load may be necessary for individuals with Type 2 Diabetes (T2D) who have a longer disease duration, larger waist circumference, increased visceral fat area, higher HbA1c levels, and elevated HOMA-IR values in order to prevent hyperglycemia.
An Approach for Detecting Dangerous Events from Online Text Using Transformers

Social media produces mass data reflecting people's opinions, emotions, and news. This data contains valuable information that can be mined and utilized to detect important events using artificial intelligence techniques. Much of the work done for detecting events in various domains is sparse and spread around different terms, often limiting the scope. Most detection methods are for extreme, negative, and anomalous events and can be considered dangerous. If detected in advance, they can help prevent them from prevailing and causing possible harm intended to individuals, groups, or society in general. This work conducts initial experiments on the usability of the proposed concept of dangerous events that suggest combining diverse events that threaten an individual or a group at different levels under a single designation - dangerous events - categorizing them into top-level and sub-level groups of dangerous events. Transformer-based models that include BERT, RoBERTa, and XLNet are implemented to detect these dangerous events from text data on different levels that can be used on social media data based on the transfer learning paradigm. The experiments reveal promising results for the proposed concept, with the BERT base model outperforming other models in most cases.
Effect of aggregate origin on freeze/thaw resistance of self-compacting concrete with and without a de-icing agent

Freezing and thawing cycles, with or without de-icing agents, are the principal causes of concrete structure degradation during the winter. This paper explores the effects of aggregate type on the level of degradation of self-compacting concrete (SCC) due to freeze-thaw (f/t) action. Natural river (NRA) and/or natural crushed (NCA) aggregate, as well as the recycled aggregate of known (RCA-N) and unknown provenance (RCA-A), were employed to produce six different SCC mixtures. The temperature, density, air content, and consistency were determined for fresh concrete, while compressive strength, water absorption by gradual immersion and vacuuming, and frost resistance with and without de-icing salts were tested for hardened concrete. Even though all tested concretes have met the criteria for frost resistance with and without the de-icing salts, it was found that the type of aggregate has a noticeable influence on it. The type of natural aggregate has little effect on SCC frost resistance, but it does influence its behavior when frost and salt are present at the same time. In f/t conditions, RCA-N can be used the same way as natural aggregate, while RCA-A causes the biggest frost resistance reduction. However, both RCAs are not recommended for application in conditions of simultaneous frost and salt impacts.
DCC-HEAVY: a multivariate GARCH model with realized measures of variance and correlation

This paper proposes a new class of multivariate volatility model that utilising high-frequency data. We call this model the DCC-HEAVY model as key ingredients are the Engle (2002) DCC model and Shephard and Sheppard (2012) HEAVY model. We discuss the models' dynamics and highlight their differences from DCC-GARCH models. Specifically, the dynamics of conditional variances are driven by the lagged realized variances, while the dynamics of conditional correlations are driven by the lagged realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed-form formulas for multi-step forecasts. Empirical results suggest that the DCC-HEAVY model outperforms the DCC-GARCH model in and out-of-sample.
Dermatophyte Infections Worldwide: Increase in Incidence and Associated Antifungal Resistance

The increase in incidence of superficial fungal infections combined with the emergence of antifungal resistance represents both a global health challenge and a considerable economic burden. Recently, dermatophytes, the main culprit causing superficial fungal infections, have started to exhibit antifungal resistance. This can be observed in some of the most common species such as Trichophyton rubrum and Trichophyton mentagrophytes. Importantly, the new subspecies, known as Trichophyton indotineae, has been reported to show high resistance to terbinafine, a first-line treatment for dermatophyte infections. Compounding these issues is the realization that diagnosing the causative infectious agents requires using molecular analysis that goes beyond the conventional macroscopic and microscopic methods. These findings emphasize the importance of conducting antifungal susceptibility testing to select the appropriate antifungal necessary for successful treatment. Implementing these changes may improve clinical practices that combat resistant dermatophyte infections.
Efficient Rural Building Segmentation via a Multilevel Decoding Network

This article addresses the problem of building segmentation for rural areas with high-resolution remote sensing images. Due to the irregular spatial distribution of rural buildings, it is often challenging to perform pixel-wise dense prediction to entire areas like the usual segmentation task to extract buildings. Specifically, we present a multilevel decoding network model that classifies the input image on the patch and image levels according to the distribution of buildings. A scene head module is used to identify scenes defined as patches that contain buildings. Depending on the scene classification results, a decode gate is taken to determine the level of prediction. This hierarchical extraction strategy reduces the amount of inference time. Experiments on our constructed rural building dataset with large-scale images validate the high efficiency of the proposed method.
The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.
Salient-Boundary-Guided Pseudo-Pixel Supervision for Weakly-Supervised Semantic Segmentation

This letter presents an innovative approach for generating pixel-wise pseudo masks as supervision for image-level Weakly Supervised Semantic Segmentation (WSSS). This is achieved by leveraging abundant object boundaries extracted with the guidance of saliency maps (SMs). Initially, we synthesize the boundary labels by combining Class Activation Maps (CAMs) and SMs. Then, an elaborately-designed joint training strategy is employed to fully exploit the complementary relationship between the foreground of CAMs and the background and boundary of SMs to yield rich object boundaries. Finally, we refine the CAMs based on the constraints imposed by the extracted boundaries, leading to more accurate pixel-wise pseudo masks. We thoroughly evaluate the performance of our proposed pseudo masks through extensive experiments, demonstrating their effectiveness as the supervision for accurate semantic segmentation. Specifically, our method achieves 71.7% mIoU and 39.1% mIoU on the validation sets of PASCAL VOC 2012 and MS COCO 2014, respectively.
Enhancing Traffic Engineering Policy in SDN Addressing TCAM limitations using Bloom Filters

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches.
Omuokdlb at the NTCIR-17 QA Lab-PoliInfo-4 Task

The omuokdlb team participated in two subtasks in NTCIR 17 QA Lab-Poliinfo-4: Question Answering-2 and Answer Verification. In Question Answering-2, we use Bidirectional Encoder Representations from Transformers (BERT) to match the question summary and the answer utterances. Then, we generated a summary of the answer to the question by using Text-to-text Transfer Transformer (T5). In Answer Verification, we created binary classifiers using BERT to determine whether or not answers, and we confirmed the effectiveness of the combination of the training data.
A Federated Deep Learning Method for Chronic Disease Diagnosis

Deep learning has become increasingly important in the diagnosis of Alzheimer’s disease due to its ability to analyze vast amounts of medical data with exceptional accuracy. The goal of this study is to conduct research on computer aided diagnosis to improve the accuracy and efficiency of diagnosing Alzheimer’s disease, and to make the diagnosis process more accessible to patients in remote or underserved areas. By training deep neural networks on large datasets, we can identify subtle patterns in patient data that may indicate the presence of Alzheimer’s disease. Furthermore, deep learning can help personalize treatment plans and monitor disease progression over time, leading to better patient outcomes. In this study, we shall introduce a novel Computer Aided Diagnosis (CAD) algorithm using transfer learning. Keeping security in mind, the models have been trained in a federated environment to ensure that satisfactory results are obtained while maintaining differential privacy.
An Investigation of Severe Influenza Cases in Russia during the 2022–2023 Epidemic Season and an Analysis of HA-D222G/N Polymorphism in Newly Emerged and Dominant Clade 6B.1A.5a.2a A(H1N1)pdm09 Viruses

In Russia, during the COVID-19 pandemic, a decrease in influenza circulation was initially observed. Influenza circulation re-emerged with the dominance of new clades of A(H3N2) viruses in 2021–2022 and A(H1N1)pdm09 viruses in 2022–2023. In this study, we aimed to characterize influenza viruses during the 2022–2023 season in Russia, as well as investigate A(H1N1)pdm09 HA-D222G/N polymorphism associated with increased disease severity. PCR testing of 780 clinical specimens showed 72.2% of them to be positive for A(H1N1)pdm09, 2.8% for A(H3N2), and 25% for influenza B viruses. The majority of A(H1N1)pdm09 viruses analyzed belonged to the newly emerged 6B.1A. 5a.2a clade. The intra-sample predominance of HA-D222G/N virus variants was observed in 29% of the specimens from A(H1N1)pdm09 fatal cases. The D222N polymorphic variant was registered more frequently than D222G. All the B/Victoria viruses analyzed belonged to the V1A.3a. 2 clade. Several identified A(H3N2) viruses belonged to one of the four subclades (2a.1b, 2a.3a.1, 2a.3b, 2b) within the 3C.2a1b. 2a.2 group. The majority of antigenically characterized viruses bore similarities to the corresponding 2022–2023 NH vaccine strains. Only one influenza A(H1N1)pdm09 virus showed reduced inhibition by neuraminidase inhibitors. None of the influenza viruses analyzed had genetic markers of reduced susceptibility to baloxavir.
Carbon quantum dots of Moringa oleifera with bactericidal action

In this work, carbon quantum dots (CQDs) were synthesized using a hydrothermal reactor. The precursor for the CQDs was Moringa oleifera seed oil, extracted using a 1:8 ethanol-to-mass ratio through ultrasound-assisted extraction (50 ºC, 30 min). The extract was then purified using a rotary evaporator (50 ºC, 50 rpm) to remove the solvent, followed by centrifugation (12,000 rpm, 10 min). Subsequently, the oil was transferred to a Teflon-lined capsule within a stainless-steel reactor and heated in a muffle furnace (210 ºC, 24 h). The synthesized CQDs were then centrifuged (12,000 rpm, 10 min) and filtered through a 0.22 μm membrane. These CQDs exhibited high fluorescence and, when activated photodynamically with white visible light, showed the ability to inhibit the growth of the Gram-positive bacterium Staphylococcus aureus, a common cause of skin infections. In recent decades, the inappropriate use of antibiotics has led to increased bacterial resistance, resulting in higher mortality rates in humans and animals. Therefore, the development of nanomaterials with antimicrobial properties, through a simple, cost-effective, and sustainable route, is essential for public health and safety.
Breast Volume Is a Predictor of Higher Heart Dose in Whole-Breast Supine Free-Breathing Volumetric-Modulated Arc Therapy Planning

In breast cancer volumetric-modulated arc therapy (VMAT) planning, the rotation of the gantry around the target implies a greater dose spreading to the whole heart, compared to tangential-field standard treatment. A consecutive cohort of 121 breast cancer patients treated with the VMAT technique was investigated. The correlation of breast volume, heart volume and lung volume with mean heart dose (mHD) and mean and maximum LAD dose (mLAD dose, MLAD dose) was tested, and a subsequent a linear regression analysis was carried out. VMAT treatment plans from 56 left breast cancer and 65 right breast cancer patients were analyzed. For right-sided patients, breast volume was significantly correlated with mHD, mLAD and MLAD dose, while for left-sided patients, breast volume was significantly correlated with mHD and mLAD, while heart volume and lung volume were correlated with mHD, mLAD and MLAD dose. Breast volume was the only predictor of increased heart and LAD dose (p ≤ 0.001) for right-sided patients. In left-sided patients, heart and lung were also predictors of increased mHD (p = 0.005, p ≤ 0.001) and mean LAD dose (p = 0.009, p ≤ 0.001). In this study, we observed an increase in heart and LAD doses in larger-breasted patients treated with VMAT planning. In right-sided patients, breast volume was shown to be the only predictor of increased heart dose and LAD dose.
DIVERSITY OF DIATOMS IN THE NATURAL, MID-FOREST TEREBOWIEC STREAM – BIESZCZADY NATIONAL PARK

The Terebowiec stream is an 8 km long watercourse which flows through the strictly protected Bieszczady National Park. The studies on bentic diatoms were conducted between 2013–2015 at three sampling sites along the Terebowiec stream and its unnamed tributary. 260 diatom taxa were identified in the streams, of which 13 taxa were considered as frequent. Chemical analysis showed good and high status of the water. In the studied samples, 27 taxa from the Polish Red List of Algae were determined.