Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinct Alterations in Dendritic Spine Morphology in the Absence of β-Neurexins

Dendritic spines are essential for synaptic function because they constitute the postsynaptic compartment of the neurons that receives the most excitatory input. The extracellularly shorter variant of the presynaptic cell adhesion molecules neurexins, β-neurexin, has been implicated in various aspects of synaptic function, including neurotransmitter release. However, its role in developing or stabilizing dendritic spines as fundamental computational units of excitatory synapses has remained unclear. Here, we show through morphological analysis that the deletion of β-neurexins in hippocampal neurons in vitro and in hippocampal tissue in vivo affects presynaptic dense-core vesicles, as hypothesized earlier, and, unexpectedly, alters the postsynaptic spine structure. Specifically, we observed that the absence of β-neurexins led to an increase in filopodial-like protrusions in vitro and more mature mushroom-type spines in the CA1 region of adult knockout mice. In addition, the deletion of β-neurexins caused alterations in the spine head dimension and an increase in spines with perforations of their postsynaptic density but no changes in the overall number of spines or synapses. Our results indicate that presynaptic β-neurexins play a role across the synaptic cleft, possibly by aligning with postsynaptic binding partners and glutamate receptors via transsynaptic columns.
Screening immune-related blood biomarkers for DKD-related HCC using machine learning

Background Diabetes mellitus is a significant health problem worldwide, often leading to diabetic kidney disease (DKD), which may also influence the occurrence of hepatocellular carcinoma (HCC). However, the relationship and diagnostic biomarkers between DKD and HCC are unclear. Methods Using public database data, we screened DKD secretory RNAs and HCC essential genes by limma and WGCNA. Potential mechanisms, drugs, and biomarkers for DKD-associated HCC were identified using PPI, functional enrichment, cMAP, and machine learning algorithms, and a diagnostic nomogram was constructed. Then, ROC, calibration, and decision curves were used to evaluate the diagnostic performance of the nomograms. In addition, immune cell infiltration in HCC was explored using CIBERSORT. Finally, the detectability of critical genes in blood was verified by qPCR. Results 104 DEGs associated with HCC using WGCNA were identified. 101 DEGs from DKD were predicated on secreting into the bloodstream with Exorbase datasets. PPI analysis identified three critical modules considered causative genes for DKD-associated HCC, primarily involved in inflammation and immune regulation. Using lasso and RM, four hub genes associated with DKD-associated HCC were identified, and a diagnostic nomogram confirmed by DCA curves was established. The results of immune cell infiltration showed immune dysregulation in HCC, which was associated with the expression of four essential genes. PLVAP was validated by qPCR as a possible blood-based diagnostic marker for DKD-related HCC. Conclusion We revealed the inflammatory immune pathways of DKD-related HCC and developed a diagnostic nomogram for HCC based on PLVAP, C7, COL15A1, and MS4A6A. We confirmed with qPCR that PLVAP can be used as a blood marker to assess the risk of HCC in DKD patients.
Impact Analysis of Social Events on Industries through Narrative Causal Search

This study focuses on discovering the ripple effects on firms of significant external events such as pandemics, resource price spikes, and natural disasters. We construct a system that presents a chain of other economic events derived from one event by extracting descriptions of causal relationships from a large amount of text data. The objective of this study is to construct a causal chain presentation system that takes into account the diversity of outputs to discover broader economic ripple effects. To accomplish this goal, we develop a new algorithm that uses Maximal Marginal Relevance to represent causal chains. Experiments using Japanese financial statement summaries confirm that this approach produces a greater variety of outputs without sacrificing accuracy. This innovative approach provides a more comprehensive understanding of how external events impact firms and their financial performance.
Patterns of Leaf and Fruit Morphological Variation in Marginal Populations of Acer tataricum L. subsp. tataricum

Marginal populations are usually smaller and more isolated and grow in less favourable conditions than those at the distribution centre. The variability of these populations is of high importance, as it can support the adaptations needed for the conditions that they grow in. In this research, the morphological variability of eight Tatar maple (Acer tataricum L. subsp. tataricum) populations was analysed. Tatar maple is an insect-pollinated and wind-dispersed shrub/tree, whose northwestern distribution edge is in southeastern Europe. Morphometric methods were used to analyse the variability of the populations using leaf and fruit morphology. The research revealed significant differences between and within populations. Furthermore, differences in the distribution of the total variability were noted, which suggest that different evolutionarily factors affect different plant traits. Correlation analysis confirmed a weak dependency between the vegetative and generative traits. In addition, no evidence was found for the presence of isolation by environment (IBE). However, the Mantel test for isolation by distance (IBD) was significant for the leaf morphometric traits and non-significant for the fruit morphometric traits. Being the marginal leading-edge populations, they are younger and were less likely to have had time for adaptation to local environments, which would have resulted in the development of IBE. Overall, edge populations of Tatar maple were characterised by great morphological variability, which helps these populations in their response to the intensive selective pressures they face in their environment.
Spatial and Temporal Variabilities of Hypoxia in the Rappahannock River

Hypoxia/anoxia in bottom waters of the Rappahannock River, a tributary estuary of Chesapeake Bay, was observed to persist throughout the summer in the deep basin near the river mouth; periodic reoxygenation of bottom water occurred on the shallower sill at the river mouth. The reoxygenation events were closely related to spring tide mixing. The dissolved oxygen (DO) in surface waters was always near or at the saturation level, while that of bottom waters exhibited a characteristic spatial pattern. The bottom DO decreased upriver from river mouth, reaching a minimum upriver of the deepest point of the river and increasing as the water became shallower further upriver. A model was formulated to describe the longitudinal distribution of DO in bottom waters. The model is based on a Lagrangian concept-following a water parcel as it travels upriver along the estuarine bottom. The model successfully describes the characteristic distribution of DO and also explains the shifting of the minimum DO location in response to spring-neap cycling. A diagnostic study with the model provided insight into relationships between the bottom DO and the competing factors that contribute to the DO budget of bottom waters. The study reveals that both oxygen demand, either benthic or water column demand, and vertical mixing have a pronounced effect on the severity of hypoxia in bottom waters of an estuary. However, it is the vertical mixing which controls the longitudinal location of the minimum DO. The strength of gravitational circulation is also shown to affect the occurrence of hypoxia. An estuary with stronger circulation tends to have less chance for hypoxia to occur. The initial DO deficit of bottom water entering an estuary has a strong effect on DO concentration near the river mouth, but its effect diminishes in the upriver direction.
Hardware Trojan Attacks on the Reconfigurable Interconnections of Field-Programmable Gate Array-Based Convolutional Neural Network Accelerators and a Physically Unclonable Function-Based Countermeasure Detection Technique

Convolutional neural networks (CNNs) have demonstrated significant superiority in modern artificial intelligence (AI) applications. To accelerate the inference process of CNNs, reconfigurable CNN accelerators that support diverse networks are widely employed for AI systems. Given the ubiquitous deployment of these AI systems, there is a growing concern regarding the security of CNN accelerators and the potential attacks they may face, including hardware Trojans. This paper proposes a hardware Trojan designed to attack a crucial component of FPGA-based CNN accelerators: the reconfigurable interconnection network. Specifically, the hardware Trojan alters the data paths during activation, resulting in incorrect connections in the arithmetic circuit and consequently causing erroneous convolutional computations. To address this issue, the paper introduces a novel detection technique based on physically unclonable functions (PUFs) to safeguard the reconfigurable interconnection network against hardware Trojan attacks. Experimental results demonstrate that by incorporating a mere 0.27% hardware overhead to the accelerator, the proposed hardware Trojan can degrade the inference accuracy of popular neural network architectures, including LeNet, AlexNet, and VGG, by a significant range of 8.93% to 86.20%. The implemented arbiter-PUF circuit on a Xilinx Zynq XC7Z100 platform successfully detects the presence and location of hardware Trojans in a reconfigurable interconnection network. This research highlights the vulnerability of reconfigurable CNN accelerators to hardware Trojan attacks and proposes a promising detection technique to mitigate potential security risks. The findings underscore the importance of addressing hardware security concerns in the design and deployment of AI systems utilizing FPGA-based CNN accelerators.
Relationship between Evapotranspiration and water Availability in the Tropical Regions: A Case Study of a South Western City in Nigeria

The understanding of the impacts of evapotranspiration on water supply in time and space is critical to a purposeful water management effort. A study was conducted to examine annual water balance situation in the humid tropical city of Ogbomoso, Oyo State, Nigeria. A thirty-year rainfall and temperature data (1977-2006) were collected from NIMET Office for this purpose. Annual evapotranspiration values were empirically determined using temperature data and Thornthwaite’s model. The study revealed that the major determinant of water balance is the availability of water at the surface which is majorly supplied by green surface and rainfall. Evapotranspiration rate was found to be relatively constant and high above 1000 mm annually ranging from 1249.7 mm in 1980 to 1112.87 mm in 2004 while annual rainfall total varies over the study period of 30 years ranging from 697.1 mm in 2002 to 1595.5 mm in 1998. Results showed that effort should be directed towards urban reforestation and or aforestation, planting of shade trees and other related measures that will encourage water availability for evapotranspiration process for improvement on water balance in the study area.
Whether Detection of Gene Mutations Could Identify Low- or High-Risk Papillary Thyroid Microcarcinoma? Data from 393 Cases Using the Next-Generation Sequencing

Objective The objective of this study is to explore the utilization of next-generation sequencing (NGS) technology in evaluating the likelihood of identifying individuals with papillary thyroid microcarcinoma (PTMC ≤10 mm) who are at high or low risk. Design NGS was used to analyze 393 formalin-fixed, paraffin-embedded tissues of PTC tumors, all of which were smaller than 15 mm. Results The study found that bilateralism, multifocality, intrathyroidal spread, and extrathyroidal extension were present in 84 (21.4%), 153 (38.9%), 16 (4.1%), and 54 (13.7%) cases, respectively. Metastasis of cervical lymph nodes was identified in 226 (57.5%) cases and 96 (24.4%) cases with CLNM >5. Out of the total number of cases studied, 8 cases (2.3%) showed signs of tumor recurrence, all of which were localized and regional. Genetic alterations were detected in 342 cases (87.0%), with 336 cases revealing single mutations and 6 cases manifesting compound mutations. 332 cases (84.5%) had BRAFV600E mutation, 2 cases had KRASQ61K mutation, 2 cases had NRASQ61R mutation, 8 cases had RET/PTC1 rearrangement, 3 cases had RET/PTC3 rearrangement, and 1 case had TERT promoter mutation. Additionally, six individuals harbored concurrent mutations in two genes. These mutations were of various types and combinations: BRAFV600E and NRASQ61R (n = 2), BRAFV600E and RET/PTC3 (n = 2), BRAFV600E and RET/PTC1 (n = 1), and BRAFV600E and TERT promoter (n = 1). The subsequent analysis did not uncover a significant distinction in the incidence of gene mutation or fusion between the cN0 and cN1 patient cohorts. The presence of BRAFV600E mutation and CLNM incidence rates were found to be positively correlated with larger tumor size in PTMC. Our data showed that gene mutations did not appear to have much to do with high-risk papillary thyroid microcarcinoma (PTMC). However, when we looked at tumor size, we found that if the tumor was at least 5 millimeters in size, there was a higher chance of it being at high risk for PTM (P < 0.001, odds ratio (OR) = 2.55, 95% confidence interval (CI): 1.57–4.14). Identification of BRAFV600E mutation was not demonstrated to be significantly correlated with advanced clinicopathological characteristics, although it was strongly associated with a bigger tumor diameter (OR = 4.92, 95% CI: 2.40–10.07, P < 0.001). Conclusion In clinical practice, BRAFV600E mutation does not consistently serve as an effective biomarker to distinguish high-risk PTMC or predict tumor progression. The size of the tumor has a significant correlation with its aggressive characteristics. PTMC with a diameter of ≤5 mm should be distinguished and targeted as a unique subset for specialized treatment.
A 2-18 GHz 6-Bit GaAs Positive Voltage Driven Digitally Controlled Attenuator for Phased Array

In this paper, an ultra-wideband 6-bit digitally controlled attenuator MMIC for the active phased array that integrates positive voltage digital driving circuits was developed, utilizing GaAs enhanced and depletion mode p-HEMT process. The attenuator design operates in an ultra-wide bandwidth of 2-18 GHz, with a dynamic attenuation range of 0.5-31.5 dB and an attenuation accuracy of less than 0.9 dB over the entire frequency band. The integrated DCFL (direct coupling field effect transistor logic) inverter circuit on the chip enables direct control of the attenuator with a positive voltage while occupying a chip area of only 2.7 mm2.
Modal Logic for Relationships between Sets

In this article, we present a modal logic system that allows representing relationships between sets or classes of individuals defined by a specific property. We introduce two modal operators, [a] and , which are used respectively to express “for all A” and “there exists an A”. Both the syntax and semantics of the system have two levels that avoid the nesting of the modal operator. The semantics is based on a variant of Kripke semantics, where the modal operators are indexed over propositional logic formulas (“pre-formulas” in the paper). Furthermore, we present a set of axioms and rules that govern the system and we prove that the logic is correct and complete with respect to Kripke models. In the final section of the article, we discuss potential future work. We consider the possibility of combining our operator with other modalities, such as necessity or knowledge. Additionally, as an example of the utility of our modal operator, we briefly analyze a conveniently adapted Barcan formula within the framework of our system. In summary, we propose combining our modal operator with other ones as a simpler, more compact, albeit less expressive way to address quantified modal logic.
Day-Ahead Energy Management of Integrated Multi-Energy Systems Considering Demand Response Scheme

This paper considers the application scenarios of microgrids in commercial parks. The user model containing multiple schedulable resources is constructed, and the energy management modes of users participating in the electricity market are analyzed. A two-stage energy management method is proposed for users considering the error of new energy generation, combining the scheduling method of daily planning and real-time regulation. In the current planning stage, users can participate in the electricity market to obtain economic benefits. The proposed solution is evaluated through a case study via simulation experiments and the numerical results confirmed the effectiveness of the proposed day-ahead energy management solution for multi-energy systems in terms of efficient utilization of renewable energy.
Smartphone Addiction and its Impacts on Loneliness, Happiness, and Satisfaction with Life in Medical Students with Different Personalities During COVID-19 Pandemic

Background: In this digital era, around the globe, smartphones have become ubiquitous and an indispensable part of everyday life. Despite being a highly productive tool, smartphone is often overused or misused leading to addiction. Studies have been carried out to assess factors influencing smartphone addiction, but very little focus has been placed on psychosocial changes in adults with different personalities, especially among medical students. Hence, this study aimed to investigate the influence of smartphone addiction on personality among medical students in South India. Methods: A validated extrovert and Introvert Personality Inventory (IPEI) was administered to assess the personality after obtaining the necessary permissions. Using a validated questionnaire, the satisfaction with life, subjective happiness, and loneliness scales were administered via Google Forms. Findings: The results showed there was a statistically significant increase in life satisfaction and loneliness scores among the extroverts in the mobile phone addiction group compared with the non-addiction group (P<0.0001). Conclusion: Considering the impact of cell phones on various factors in adults in different areas can complement the nationwide statistics so as to formulate a health policy to address the vulnerable population and thus prevent the deterioration of the psychosocial behavior of the young generation.
Plant Leaf Disease Detection using Novel Machine Learning Approach

Plant leaf disease detection is a critical component of modern agriculture, aimed at early and accurate identification of diseases that can impact crop yields and food security. But the existing detection models attain insufficient and low-quality when utilizing the large amount of data. To overcomes this issue, a novel Machine Learning (ML) algorithm namely, Logistic Regression based Ensemble Classifier (LREC) is proposed for detecting the plant leaf disease. The data are gathered from the leaf image dataset and collected images are pre-processed to remove noise and Histogram equalization is utilized to enhance the quality of the image. Principal Component Analysis (PCA) is performed in feature extraction process and classified through Logistic regression integrated with ensemble classifier. When compared with existing method, the proposed method attains the better performance in detection of plant leaf diseases. The results are outperformed with evaluation metrics such as precision, recall, accuracy and f-measure as respectively.
274-GHz Substrate-Integrated-Waveguide Fed Rotman-Lens Multibeam Antenna Integrated in Multi-Layer Substrate

A multibeam antenna fed by a substrate-integrated-waveguide (SIW) Rotman-lens beam-forming network (BFN) was designed at 274 GHz. A Rotman lens and microstrip comb-line antennas are placed in the different layers within a single 7-layer substrate to reduce the 2D size to 11 mm × 10.5 mm. The simulated radiation patterns show that the proposed antenna achieves beam scanning over 80 degrees.
A Compact Broadside Coupled Stripline 2-D Beamforming Network and Its Application to a 2-D Beam Scanning Array Antenna Using Panasonic Megtron 6 Substrate

This article presents a 4-way 2-D butler matrix (BM)-based beamforming network (BFN) using a multilayer substrate broadside coupled stripline (BCS). To achieve the characteristics of a compact, wide-bandwidth, high-gain phased array, a BCS coupler is implemented using the Megtron 6 substrate. The compact 2-D BFN is formed by combining planarly two horizontal BCS couplers and two vertical BCS couplers. The BFN is proposed without a crossover and without a phase shifter, generating phase responses of ±90° in the x- and y-directions, respectively. The proposed BFN exhibits a wide operating band of 66.7% (3–7 GHz) and a compact physical area of just 0.25 λ0 × 0.25 λ0 × 0.04 λ0. The planar 2-D BFN is easily integrated with the patch antenna radiation elements to construct a 2-D multibeam array antenna that generates four fixed beams, one in each quadrant, at an elevation angle of 30° from the broadside to the array axis when the element separation is 0.6 λ0. The physical area of the 2-D multibeam array antenna is just 0.8 λ0 × 0.8 λ0 × 0.04 λ0. The prototypes of the BCS coupler, the 2-D BFN, and the 2-D multibeam array antenna were fabricated and measured. The measured and simulated results were in good agreement. A gain of 9.1 to 9.9 dBi was measured.
Gene Association Disease Prediction by GEO2R Tool

Psychiatric illnesses have a profound impact on people's mental health, their ability to function, and their general quality of life. Disorders such as bipolar disorder are very complicated and influenced by several factors, including genetic variations, behavioral characteristics, and the environment in which a person lives. In this study, the evaluation is done using the gene data of humans suffering from bipolar disorder using a tool from NCBI (National Center for Biotechnology Information) called GEO2R. This is a tool that compares two or more groups of Samples from the predefined GEO datasets and identifies the genes that are differentially expressed in different conditions. Based on the comparison the results are expressed in the form of a table of the top 250 genes which are placed sequentially according to the adjusted P-values. Further, these results are presented in the form of several graphical plots.