Miracle-dz/UniFAR-FTUs huggingface dataset:
Meteorology-Assisted Spatio-Temporal Graph Network for Uncivilized Urban Event Prediction

Uncivilized urban events disrupt urban order and have a detrimental impact on daily life. Recognizing the significant implications of these events, urban managers strive to proactively prevent them by accurately predicting their future occurrence. However, existing methods overlook crucial contextual information within urban scenarios while mining spatio-temporal dependencies in single event series. Fortunately, we discovered a connection between meteorological conditions and uncivilized events. To leverage this relationship, we propose a novel approach named the Meteorology-Assisted Spatio-Temporal Graph Neural Network (MAST) which integrates meteorological information into the spatio-temporal dependency modeling for predicting urban uncivilized events. Additionally, our approach captures latent regularities in human behavior by explicitly modeling individuals’ psychological states based on meteorological information. We also adopt cross-view contrastive learning between urban regions to dynamically capture the informative components of meteorological information for precise prediction of urban uncivilized events. Experimental evaluations on a real-world dataset demonstrate the superiority of MAST over state-of-theart baselines in terms of predictive performance.
Inhibiting Liver Autophagy and Promoting Hepatocyte Apoptosis by Schistosoma Japonicum Infection

We established a mouse model of Schistosoma japonicum infection in order to study the effects of the infection on hepatocyte autophagy and apoptosis. We also stimulated HepG2 cells with soluble egg antigens (SEA) in vitro. At two, four, and six weeks post-infection, quantitative real-time PCR and Western blot (WB) were used to detect liver expression levels of autophagy and apoptosis-related proteins. HepG2 cells were treated with different concentrations of SEA. The changes in the levels of autophagy-related proteins and HepG2 cell apoptosis were detected. The Lc3b, Beclin1, Atg7, and Atg12 mRNA levels were significantly lower at four and six weeks after infection than those in the uninfected group. At four and six weeks following infection, the levels of Beclin1, LC3BII/I, Atg7, and p62 proteins were considerably lower than those in the uninfected group. The protein levels of pro-apoptotic Bax and cleaved caspase 3 and fibrosis-related proteins α-SMA and collagen 3 in the liver post-infection were significantly higher than those in uninfected mice. HepG2 cells stimulated with SEA showed decreased levels of Beclin1, p62, and Atg7 proteins and significantly increased apoptosis rates. The findings demonstrated that following infection with S. japonicum, mice’s liver fibrosis worsened, hepatic autophagy was suppressed, and hepatocyte apoptosis was encouraged.
Gan Class E Wireless Power Transfer System: A New Design Method Relying on the Advanced Modeling of Parasitic Elements

We report the design, the simulation and the characterization of a 13.56 MHz resonant inductive coupling wireless power transfer (WPT) system based on a class E inverter. The main benefit of a class E WPT system is the small amount of components needed while achieving good efficiency. An analytical model of the system is proposed and a new design method is reported. The fabricated system shows an efficiency of 76 % for a transmitted power of 3.4 W.
Flood did not cause the taxonomic and functional homogenization of Chironomidae (Diptera) community in a Neotropical floodplain

Abstract: Aim We investigated the hypothesis that flood decreases the taxonomic and functional beta diversity of Chironomidae (Diptera) larvae among floodplain lakes, causing biotic homogenization. Methods We collected benthic samples using a modified Petersen grab in 18 lakes in a flood period (March 2011) and a drought period (September 2011) in the Upper Paraná River floodplain. We compared the taxonomic and functional beta diversity of Chironomidae among lakes in flood and drought periods using a multivariate dispersion analysis (PERMDISP). Results We found similar taxonomic and functional beta diversity of the Chironomidae between hydrological periods. Conclusions We did not find evidence of biotic homogenization due to flood pulse. Our findings suggest other mechanisms besides increasing connectivity and environmental similarity due to flood pulse that may structure the benthic community, such as variation in the identity of flood-tolerant morphospecies across lakes.
Norepinephrine-Activated p38 MAPK Pathway Mediates Stress-Induced Cytotoxic Edema of Basolateral Amygdala Astrocytes

The amygdala is a core region in the limbic system that is highly sensitive to stress. Astrocytes are key players in stress disorders such as anxiety and depression. However, the effects of stress on the morphology and function of amygdala astrocytes and its potential mechanisms remain largely unknown. Hence, we performed in vivo and in vitro experiments using a restraint stress (RS) rat model and stress-induced astrocyte culture, respectively. Our data show that norepinephrine (NE) content increased, cytotoxic edema occurred, and aquaporin-4 (AQP4) expression was up-regulated in the basolateral amygdala (BLA) obtained from RS rats. Additionally, the p38 mitogen-activated protein kinase (MAPK) pathway was also observed to be significantly activated in the BLA of rats subjected to RS. The administration of NE to in vitro astrocytes increased the AQP4 level and induced cell edema. Furthermore, p38 MAPK signaling was activated. The NE inhibitor alpha-methyl-p-tyrosine (AMPT) alleviated cytotoxic edema in astrocytes, inhibited AQP4 expression, and inactivated the p38 MAPK pathway in RS rats. Meanwhile, in the in vitro experiment, the p38 MAPK signaling inhibitor SB203580 reversed NE-induced cytotoxic edema and down-regulated the expression of AQP4 in astrocytes. Briefly, NE-induced activation of the p38 MAPK pathway mediated cytotoxic edema in BLA astrocytes from RS rats. Thus, our data provide novel evidence that NE-induced p38 MAPK pathway activation may be one of the mechanisms leading to cytotoxic edema in BLA under stress conditions, which also could enable the development of an effective therapeutic strategy against cytotoxic edema in BLA under stress and provide new ideas for the treatment of neuropsychiatric diseases.
Association of Alpha-Crystallin with Human Cortical and Nuclear Lens Lipid Membrane Increases with the Grade of Cortical and Nuclear Cataract

Eye lens α-crystallin has been shown to become increasingly membrane-bound with age and cataract formation; however, to our knowledge, no studies have investigated the membrane interactions of α-crystallin throughout the development of cataracts in separated cortical membrane (CM) and nuclear membrane (NM) from single human lenses. In this study, four pairs of human lenses from age-matched male and female donors and one pair of male lenses ranging in age from 64 to 73 years old (yo) were obtained to investigate the interactions of α-crystallin with the NM and CM throughout the progression of cortical cataract (CC) and nuclear cataract (NC) using the electron paramagnetic resonance spin-labeling method. Donor health history information (diabetes, smoker, hypertension, radiation treatment), sex, and race were included in the data analysis. The right eye lenses CM and NM investigated were 64 yo male (CC: 0), 68 yo male (CC: 3, NC: 2), 73 yo male (CC: 1, NC: 2), 68 yo female (CC: 3, NC: 2), and 73 yo female (CC: 1, NC: 3). Similarly, left eye lenses CM and NM investigated were 64 yo male (CC: 0), 68 yo male (CC: 3, NC: 2), 73 yo male (CC: 2, NC: 3), 68 yo female (CC: 3, NC: 2), and 73 yo female (CC: 1, NC: 3). Analysis of α-crystallin binding to male and female eye lens CM and NM revealed that the percentage of membrane surface occupied (MSO) by α-crystallin increases with increasing grade of CC and NC. The binding of α-crystallin resulted in decreased mobility, increased order, and increased hydrophobicity on the membrane surface in male and female eye lens CM and NM. CM mobility decreased with an increase in cataracts for both males and females, whereas the male lens NM mobility showed no significant change, while female lens NM showed increased mobility with an increase in cataract grade. Our data shows that a 68 yo female donor (long-term smoker, pre-diabetic, and hypertension; grade 3 CC) showed the largest MSO by α-crystallin in CM from both the left and right lens and had the most pronounced mobility changes relative to all other analyzed samples. The variation in cholesterol (Chol) content, size and amount of cholesterol bilayer domains (CBDs), and lipid composition in the CM and NM with age and cataract might result in a variation of membrane surface mobility, membrane surface hydrophobicity, and the interactions of α-crystallin at the surface of each CM and NM. These findings provide insight into the effect of decreased Chol content and the reduced size and amount of CBDs in the cataractous CM and NM with an increased binding of α-crystallin with increased CC and NC grade, which suggests that Chol and CBDs might be a key component in maintaining lens transparency.
An Explainable Deep Learning-based Approach for Multivariate Time Series Anomaly Detection in IoT

Detecting anomalies from Internet of Things (IoT) data is important for a smooth flow of events. Timely detection of anomalies and raising alarms can help avoid any potential health and safety hazards in an IoT environment such as detecting falling people by activity recognition using wearable sensors. However, most IoT data lacks labels and sometimes when the labels are available, the context is missing, which makes anomaly detection and inference a challenging task. In this paper, we propose an explainable deep learning-based approach to detect anomalies from multivariate time series data using Long Short-Term Memory (LSTM) and the results are explained using the Local Interpre table Model-agnostic Explanations (LIME). The experiments show promising classification results with good accuracy, precision, recall, and F1 Score values. LIME provided useful explanations for anomalous and non-anomalous instances. The effectiveness of LIME for time series anomaly detection is discussed, and its limitations are also highlighted.
Comparative transcriptomics analysis identifies crucial genes and pathways during goose spleen development

As the largest peripheral lymphoid organ in poultry, the spleen plays an essential role in regulating the body’s immune capacity. However, compared with chickens and ducks, information about the age- and breed-related changes in the goose spleen remains scarce. In this study, we systematically analyzed and compared the age-dependent changes in the morphological, histological, and transcriptomic characteristics between Landes goose (LG; Anser anser) and Sichuan White goose (SWG; Anser cygnoides). The results showed a gradual increase in the splenic weights for both LG and SWG until week 10, while their splenic organ indexes reached the peak at week 6. Meanwhile, the splenic histological indexes of both goose breeds continuously increased with age, reaching the highest levels at week 30. The red pulp (RP) area was significantly higher in SWG than in LG at week 0, while the splenic corpuscle (AL) diameter was significantly larger in LG than in SWG at week 30. At the transcriptomic level, a total of 1710 and 1266 differentially expressed genes (DEGs) between week 0 and week 30 were identified in spleens of LG and SWG, respectively. Meanwhile, a total of 911 and 808 DEGs in spleens between LG and SWG were identified at weeks 0 and 30, respectively. Both GO and KEGG enrichment analysis showed that the age-related DEGs of LG or SWG were dominantly enriched in the Cell cycle, TGF−beta signaling, and Wnt signaling pathways, while most of the breed-related DEGs were enriched in the Neuroactive ligand−receptor interaction, Cytokine−cytokine receptor interaction, ECM−receptor interaction, and metabolic pathways. Furthermore, through construction of protein-protein interaction networks using significant DEGs, it was inferred that three hub genes including BUB1, BUB1B, and TTK could play crucial roles in regulating age-dependent goose spleen development while GRIA2, GRIA4, and RYR2 could be crucial for the breed-specific goose spleen development. These data provide novel insights into the splenic developmental differences between Chinese and European domestic geese, and the identified crucial pathways and genes are helpful for a better understanding of the mechanisms regulating goose immune functions.
Who can benefit from postmastectomy radiotherapy among HR+/HER2- T1-2 N1M0 breast cancer patients? An explainable machine learning mortality prediction based approach

Objective The necessity of postmastectomy radiotherapy(PMRT) for patients with HR+/HER2 T1-2 N1M0 breast cancer remains controversial. We want to use explainable machine learning to learn the feature importance of the patients and identify the subgroup of the patients who may benefit from the PMRT. Additionally, develop tools to provide guidance to the doctors. Methods In this study, we trained and validated 2 machine learning survival models: deep learning neural network and Cox proportional hazard model. The training dataset consisted of 35,347 patients with HR+/HER2- T1-2 N1M0 breast cancer who received mastectomies from the SEER database from 2013 to 2018. The performance of survival models were assessed using a concordance index (c-index). Then we did subgroup analysis to identify the subgroup who could benefit from PMRT. We also analyzed the global feature importance for the model and individual feature importance for individual survival prediction. Finally, we developed a Cloud-based recommendation system for PMRT to visualize the survival curve of each treatment plan and deployed it on the Internet. Results A total of 35,347 patients were included in this study. We identified that radiotherapy improved the OS in patients with tumor size >14mm and age older than 54: 5-year OS rates of 91.9 versus 87.2% (radio vs. nonradio, P <0.001) and cohort with tumor size >14mm and grade worse than well-differentiated, 5-year OS rates of 90.8 versus 82.3% (radio vs. nonradio, P <0.001). The deep learning network performed more stably and accurately in predicting patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.776 vs 0.641) and in the external validation(C-index=0.769 vs 0.650). Besides, the deep learning model identified several key factors that significantly influence patient survival, including tumor size, examined regional nodes, age at 45-49 years old and positive regional nodes (PRN). Conclusion Patients with tumor size >14mm and age older than 54 and cohort with tumor size >14mm and grade worse than well-differentiated could benefit from the PMRT. The deep learning network performed more stably and accurately in predicting patients survival than Cox proportional hazard model on the internal test. Besides, tumor size, examined regional nodes, age at 45-49 years old and PRN are the most significant factors to the overall survival (OS).
Adhesion-clutch between DCC and netrin-1 mediates netrin-1–induced axonal haptotaxis

The growth cone, a motile structure located at the tip of growing axons, senses extracellular guidance cues and translates them into directional forces that drive axon outgrowth and guidance. Axon guidance directed by chemical cues on the extracellular adhesive substrate is termed haptotaxis. Recent studies reported that netrin-1 on the substrate functions as a haptotactic axon guidance cue. However, the mechanism mediating netrin-1–induced axonal haptotaxis remains unclear. Here, we demonstrate that substrate-bound netrin-1 induces axonal haptotaxis by facilitating physical interactions between the netrin-1 receptor, DCC, and the adhesive substrates. DCC serves as an adhesion receptor for netrin-1. The clutch-linker molecule shootin1a interacted with DCC, linking it to actin filament retrograde flow at the growth cone. Speckle imaging analyses showed that DCC underwent either grip (stop) or retrograde slip on the adhesive substrate. The grip state was more prevalent on netrin-1–coated substrate compared to the control substrate polylysine, thereby transmitting larger traction force on the netrin-1–coated substrate. Furthermore, disruption of the linkage between actin filament retrograde flow and DCC by shootin1 knockout impaired netrin-1–induced axonal haptotaxis. These results suggest that the directional force for netrin-1–induced haptotaxis is exerted on the substrates through the adhesion-clutch between DCC and netrin-1 which occurs asymmetrically within the growth cone.
Predicting Corporate Solvency using Sentiment Analysis of Risk Analyst Textual Assessments

Credit scoring has long been crucial to financial institutions’ decision-making processes, providing a means to evaluate the risk of extending credit to consumers and businesses. With recent advances in artificial intelligence (AI), decision-making algorithms have become more precise and efficient, leading to a wide diversity of applications across various industries. The financial sector has increasingly adopted AI-powered algorithms and advanced data analytics for tasks such as credit scoring and pricing. Traditional credit risk analysts, however, have relied on qualitative data, such as news articles and press releases, to assess a company’s situation before granting credit. This paper introduces a novel framework that combines credit risk text assessments made by credit analysts with a pre-trained financial sentiment analysis model. By comparing the sentiment analysis of the text with the actual ratings given by risk analysts, we demonstrate the effectiveness of our proposed framework.
Numerical Modelling Of Flow And Air Chemistry In A Large Alpine Valley

The Mesolcina valley in the Alps south of the San Bernardino pass has been chosen for the VOTALP valley experiment in order to study the formation of photo-oxidants and the thermally driven transport of pollutants into an alpine valley from the surrounding forelands. High ozone concentrations have been frequently reported from this and adjacent valleys during photosmog-episodes. VOTALP is an EU project dealing with ozone transport in the Alps. Surface, radiosonde, and aircraft measurements have been made during two summersmog episodes in 1996 in this valley. Afterwards a combined mesoscale meteorology-chemistry model was used to simulate numerically the second of the two smog episodes. One of the main goals of this valley experiment was to investigate the importance of in-situ production of photo-oxidants in Alpine valleys compared to the advection from the plains outside the mountains
Prediction in Long-term Evolution: Exploiting the Interaction Between Urban Crowd Flow Variation and POI Transition Patterns

Long-term urban crowd flow prediction involving the evolution trends of crowd flow is of great importance of traffic management, public safety and urban planning. However, learning long-term crowd flow is very challenging due to the latent effect of varied urban Point-of-Interests distribution, which is quite different from the short-term crowd flow mainly influenced by readily available external factors like weather, date, etc. The key issue for us is how to learn the interaction between POI distribution and human mobility in a dynamic way. To address this problem, we propose a POI-flow interaction based spatial-temporal framework (PFIST) for long-term crowd flow prediction. First, we model the long-term evolution representations of crowd flow and POI distribution. Then we study the dynamic interaction between POI transition patterns and crowd flow variation on different POI periods and categories. Afterwards, we decompose the flow sequence into long-term trend and daily variation parts and apply the normalized POI-flow interaction attention to the long-term trend parts. Finally, we model the spatial and multi-scale temporal dependencies to predict long-term crowd flow. Extensive experiments on Beijing map query track dataset and NYC taxi dataset demonstrate the superiority of PFIST.
GeoMixer: The MLP-Based Sequential POI Recommender with Travel Routing Modelling

Nowadays, with the rise of location-based services, the personalized sequential POI recommendation has become a pivotal element for enhancing customer experiences. Although many previous POI recommendation models have shown promising results and improvements in this area, several challenges still exist in this field. Firstly, the previous sequential recommenders do not well-utilize the geographical features that are highly affecting the user’s future choices of visits. Furthermore, the self-attention mechanism, which is a popular method used in sequential POI recommendation, has a limitation in treating the input user sequence as an unordered set. Using positional embedding is a typical way to overcome this limitation. However, the use of such embeddings may potentially restrict the model’s ability to learn meaningful patterns in user preferences among POIs. To address these challenges, we propose GeoMixer, a novel MLP-based sequential POI recommender that incorporates travel routing distance to capture geographical features and leverages Multi-layer Perceptron (MLP) architecture to model the spatial and sequential patterns in the sequential POI recommendations. By adopting MLP mixing layers, GeoMixer has the capability of memorizing the chronological order of the input POIs without the positional embedding and can emphasize the important latent features of each POI. The use of the travel routing information improves the model’s ability of capturing spatial patterns during the model learning process. Extensive experiments on real-world datasets show that GeoMixer outperforms state-of-theart methods in various metrics, highlighting the significance of incorporating travel routing distance and leveraging MLP architecture in sequential POI recommendation systems.