Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pilot Study Exploring the Association of Entacapone, Gut Microbiota, and the Subsequent Side Effects in Patients With Parkinson’s Disease

Background and Aims Entacapone, one of the most common drugs distributed among patients with Parkinson’s disease, is a peripherally acting catechol-O-methyltransferase (COMT) inhibitor that is used in addition to levodopa to control symptoms. However, there have been negative effects reported against entacapone, namely, gastrointestinal (GI) problems and drowsiness. In this pilot study, we aim to examine the hypothesis that the discomfort induced by entacapone might be originated from the shift of microbial composition by adjusting the effect of levodopa. Methods The population in this pilot study consisted of 13 PD patients treated with levodopa only and 11 with both levodopa and entacapone. The 16S rRNA gene sequence data were processed, aligned, and categorized using the DADA2. Alpha diversity indices for Observed, Chao1, Shannon, and Simpson metrics were calculated with Phyloseq 1.32. 0. Dissimilarities were calculated using unweighted unique fraction metrics (Unifrac), weighted Unifrac, and Canberra distance. Functional differences were calculated by PICRUSt2 based on the KEGG database. Results Results of 16S rRNA sequencing analysis showed that while entacapone did not influence the species richness, the composition of the microbial community shifted considerably. Relative abundances of bacteria related to constipation and other GI disorders also altered significantly. Functional enrichment analysis revealed changes in the metabolic activity of alanine, aspartate, and glutamate. These amino acids are related to common side effects of entacapone such as auditory hallucinations, fatigue, and nightmare. Conclusion Our findings provide testable hypothesis on the cause of unpleasant side effects of entacapone, which in the long run could possibly be reduced through gut microbiota manipulation.
TNF Signaling Acts Downstream of MiR-322/-503 in Regulating DM1 Myogenesis

Myotonic dystrophy type 1 (DM1) is caused by the expanded CUG repeats and usually displays defective myogenesis. Although we previously reported that ectopic miR-322/-503 expression improved myogenesis in DM1 by targeting the toxic RNA, the underlying pathways regulating myogenesis that were aberrantly altered in DM1 and rescued by miR-322/-503 were still unknown. Here, we constructed DM1 and miR-322/-503 overexpressing DM1 myoblast models, which were subjected to in vitro myoblast differentiation along with their corresponding controls. Agreeing with previous findings, DM1 myoblast showed remarkable myogenesis defects, while miR-322/-503 overexpression successfully rescued the defects. By RNA sequencing, we noticed that Tumor necrosis factor (TNF) signaling was the only pathway that was significantly and oppositely altered in these two experimental sets, with it upregulated in DM1 and inhibited by miR-322/-503 overexpression. Consistently, hyperactivity of TNF signaling was detected in two DM1 mouse models. Blocking TNF signaling significantly rescued the myogenesis defects in DM1. On the contrary, TNF-α treatment abolished the rescue effect of miR-322/-503 on DM1 myogenesis. Taking together, these results implied that TNF signaling mediated the myogenesis defects in DM1 and might act downstream of miR-322/-503 in regulating the myogenesis in DM1. Moreover, the inhibition of TNF signaling benefiting myogenesis in DM1 provided us with a novel therapeutic strategy for DM1.
Passive Component Weight Reduction for Three Phase Power Converters

Over the past ten years, there has been increased use of electronic power processing in alternative, sustainable, and distributed energy sources, as well as energy storage systems, transportation systems, and the power grid. Three-phase voltage source converters (VSCs) have become the converter of choice in many ac mediumand high-power applications due to their many advantages, such as high efficiency and fast response. For transportation applications, high power density is the key design target, since increasing power density can reduce fuel consumption and increase the total system efficiency. While power electronics devices have greatly improved the efficiency, overall performance and power density of power converters, using power electronic devices also introduces EMI issues to the system, which means filters are inevitable in those systems, and they make up a significant portion of the total system size and cost. Thus, designing for high power density for both power converters and passive components, especially filters, becomes the key issue for three-phase converters. This dissertation explores two different approaches to reducing the EMI filter size. One approach focuses on the EMI filters itself, including using advanced EMI filter structures to improve filter performance and modifying the EMI filter design method to avoid overdesign. The second approach focuses on reducing the EMI noise generated from
Performance Analysis of AC-DC Electrical Capacitance Tomography

The electrical capacitance tomography (ECT) is a method to determine the material distribution within the interior of a closed object by measuring the capacitance value across externally mounted electrodes. This paper investigates the electrical capacitance tomography measure up to performance of triangle wave (AC) and charge-discharge (DC) methods. AC-DC based method applying voltage to the ECT sensor with different time. An excitation signal is applied to a pair of electrodes that forms a capacitor during each measurement step in order to determine the capacitance from the output current measured. When the voltages is applied to the pair of electrodes, the analyzing charging and discharging time and quality of the output signal. The betterment of the ECT system need to avoid stray capacitance then increase speed to switching combinational electrodes. This paper presents finally, the output voltages are also convert into images by using MAT Lab.
Smart Chunk Reader: Stochastic Neural Chunk Selection for SQuAD

We build a question answering (QA) system and apply it on the Stanford Question Answering Dataset (SQuAD 2.0) [1], seeking to achieve high accuracy scores (EM, F1, AvNA) given default project constraints. We sought to build an "intelligent" implementation of Yu et al.’s Dynamic Chunk Reader (DCR) [2] that we dub Smart Chunk Reader (SCR), primarily through feeding DCR with top answer candidates and weightings from a performant candidate model, Stochastic Answer Networks (SAN) [3] from Liu et al. Using our performant SAN model, we achieve an F1 score of 60.34 and an EM score of 57.14 on the unseen SQuAD 2.0 test set.
Nutritional quality of mixed silages of Saccharum officinarum L. and leaves from Manihot esculenta

Objective : To evaluate the inclusion of different proportions of leaves from Manihot esculenta Crantz in the nutritional quality of Sacharum officinarum L. silages in Argentina. Materials and Methods : A complete randomized design and three replicas per treatment were used: T1) M. esculenta (30 %): S. officinarum (70 %); T2) M. esculenta (50 %): S. officinarum (50 %) and T3) M. esculenta (70 %): S. officinarum (30 %). At 60 days of the silage the percentage content of dry matter, crude protein, acid detergent fiber, phosphorus, calcium, potassium and magnesium, was determined. Digestibility by formula, total digestible nutrients and digestible energy, were estimated. The organoleptic and pH characteristics were determined. A variance analysis and Tukey’s test at 0,05 % were used for the separation of means among treatments. Results : The crude protein had values between 8,4 (T1) and 16,0 % (T3). The aggregation of 70,0 % of M. esculenta leaves in the mixture increased the protein value in 90,0 and 25,5 % with regards to treatments T1 and T2. The acid detergent fiber was lower in T3 and, consequently, digestibility and total digestible nutrients increased in this treatment compared with the others. Conclusions : The inclusion of M. esculenta leaves improved the nutritional quality of the S. officinarum silage, by increasing, proportionally and significantly, the protein values, digestibility and total digestible nutrients. The ensiled material showed an adequate conservation process.
Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search

Gender bias is one of the most common and well-studied demographic biases in information retrieval, and in general in AI systems. After discovering and reporting that gender bias for certain professions could change searchers' worldviews, mainstreaming image search engines, such as Google, quickly took action to correct and fix such a bias. However, given the nature of these systems, viz., being opaque, it is unclear if they addressed unequal gender representation and gender stereotypes in image search results systematically and in a sustainable way. In this paper, we propose adversarial attack queries composed of professions and countries (e.g., 'CEO United States') to investigate whether gender bias is thoroughly mitigated by image search engines. Our experiments on Google, Baidu, Naver, and Yandex Image Search show that the proposed attack can trigger high levels of gender bias in image search results very effectively. To defend against such attacks and mitigate gender bias, we design and implement three novel re-ranking algorithms -- epsilon-greedy algorithm, relevance-aware swapping algorithm, and fairness-greedy algorithm, to re-rank returned images for given image queries. Experiments on both simulated (three typical gender distributions) and real-world datasets demonstrate the proposed algorithms can mitigate gender bias effectively.
Emergency Physicians' Preparedness for CBRNE Incidents in Hong Kong

Background Recent terrorist attacks with biological and chemical weapons have caused much concern for the public. The purpose of this study was to assess emergency doctors' preparedness in handling Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE) incidents. Methods This is a cross sectional survey of all emergency doctors working in public emergency departments (ED) in Hong Kong. Apart from the experience and confidence to handle these events, data on participants' willingness to participate in CBRNE incidents and training motivations were also analysed. Results Biological and radiological events were found to be the least commonly encountered. Few respondents reported they were confident to manage biological events (10.6% to 29.8%), despite 66% of respondents reported having prior training in CBRNE management. Conclusions This study demonstrates ED doctors' low confidence in managing specific CBRNE incidents. Current strengths identified include the good awareness of hospital emergency plans and high motivation to get more training.
Improved Task Planning through Failure Anticipation in Human-Robot Collaboration

Human-Robot Collaboration (HRC) has become a major trend in robotics in recent years with the idea of combining the strengths from both humans and robots. In order to share the work to be done, many task planning approaches have been implemented. However, they don't fully satisfy the required adaptability in human-robot collaborative tasks, with most approaches not considering neither the state of the human partner nor the possibility of adapting the collaborative plan during execution or even anticipating failures. In this paper, we present a planning system for human-robot collaborative plans that takes into account the agents' states and deals with unforeseen human behaviour, by replanning in anticipation when the human state changes to prevent action failure. The human state is defined in terms of capacity, knowledge and motivation. The system has been implemented in a standardised environment using the Planning Domain Definition Language (PDDL) and the modular ROSPlan framework, and we have validated the approach in multiple simulation settings. Our results show that using the human model fosters an appropriate task allocation while allowing failure anticipation, replanning in time to prevent it.
Contact Point Layout Effects on Gripping Device

A mathematic model of point contact friction force between gripper jaw and workpiece was established based on the wrench theory and the principle of force equivalence. The contact point layout was considered and its effects on the gripping force of the jaw was analyzed. The model reveals the variation rule of gripping capability under time-varying heavy load conditions, which can provide theoretical basis and technical support for the flexible operation and stable gripping of manipulators under complex conditions.
Recipe Text Classification using Graph Neural Networks

Recipe text classification is used to categorize new recipe’s based on the knowledge acquired from training dataset. Here we utilized Graph Convolution Neural Networks to perform the text classification. Graph Convolution Networks are very effective in non euclidean domains where complex relationships and inter-dependency between the objects are represented as graph. Graph Convolution neural networks are used to classify Food recipe text into various categories (like vegetarian, meat etc.,) and compare it with baseline of Long Short Term Memory (LSTM) neural network. we observed better validation performance with GCN compared to LSTM on same train and test dataset. Also GCN quickly achieves higher train and test accuracy at very early epoch compared to LSTM.
Exploring the Impacts of Character Modeling, Co-Attention, and Dual Attention on BiDAF Performance

Question-answering is a key area of focus in the NLP community, and has been improved in recent years with the implementation of neural attention mechanisms. The BiDirectional attention Flow (BiDAF) has made strides in question-answering. However, more complex attention mechanisms have been developed in recent years that may further improve question-answering models. In this paper, I explore the impacts of added character-level embeddings, a CoAttention mechanism
Mitigation of chilling injury in sweet potato roots subjected to low-temperature conditioning

In many plant tissues, low-temperature conditioning (LTC) has been used to induce postharvest adaptive responses to overcome chilling injury (CI) consequences. This study aimed to investigate whether LTC is effective in reducing the incidence of chilling injury (CI) in sweet potato cultivars. Sweet potato roots BRS Cuia and BRS Rubissol were submitted to cold storage (6 °C or 13 °C) and LTC (10 °C for 7 d, followed by second storage at 6 °C). LTC increased the tolerance of sweet potato roots to CI via antioxidant system activation, mainly in cv. BRS Rubissol, which was markedly associated with higher content of both total soluble phenolics and proline, as well as increased activity of phenylalanine ammonium lyase. Moreover, no either external or internal injuries were observed in the LTC roots, maintaining the quality for processing, which indicates that the LTC is effective in mitigating chilling injury in Brazilian sweet potato cultiva
Consequences of an ecosystem state shift for nitrogen cycling in a desert stream

Cessation of cattle grazing has resulted in the reestablishment of wetlands in some streams of the U.S. Southwest. Decades of cattle grazing prevented vascular plant growth in Sycamore Creek (Arizona, U.S.A.), resulting in stream reaches dominated by diatoms and filamentous green algae. Establishment of vascular plants can profoundly modify ecosystem processes; yet, the effects on nitrogen (N) cycling remain unexplored. We examined the consequences of this ecosystem state shift on N cycling in this N‐limited desert stream. We compared results from whole‐reach ammonium‐N stable isotope (15NH4+) tracer additions conducted before (pre‐wetland state) and 13 yr after (wetland state) free‐range cattle removal from the watershed. Water column estimations showed that in‐stream N uptake and storage were higher in the pre‐wetland than in the wetland state. N turnover was also higher in the pre‐wetland state, indicating that assimilated N was retained for shorter time in stream biomass. In addition, N uptake was mostly driven by assimilatory uptake regardless of the ecosystem state considered. Water column trends were mechanistically explained by the fact that the dominant primary uptake compartments in the pre‐wetland state (i.e., algae and diatoms) had higher assimilatory uptake and turnover rates than those in the wetland state (i.e., vascular plants). Overall, results show that the shift in the composition and dominance of primary producers induced by the cessation of cattle grazing within the stream‐riparian corridor changes in‐stream N processing from a dominance of intense and fast N recycling to a prevalence of N retention in biomass of primary producers.
Simplified Performance-Based Analysis for Seismic Slope Displacements

Simplified Performance-Based Analysis for Seismic Slope Displacements Marlem Lucia Astorga Mejia Department of Civil and Environmental Engineering, BYU Master of Science Millions of lives have been lost over the years as a result of the effects of earthquakes. One of these devastating effects is slope failure, more commonly known as landslide. Over the years, seismologists and engineers have teamed up to better record data during an earthquake. As technology has advanced, the data obtained have become more refined, allowing engineers to use the data in their efforts to estimate earthquakes where they have not yet occurred. Several methods have been proposed over time to utilize the earthquake data and estimate slope displacements. A pioneer in the development of methods to estimate slope displacements, Nathan Newmark, proposed what is now called the Newmark sliding block method. This method explained in very simple ways how a mass, in this case a rigid block, would slide over an incline given that the acceleration of the block surpassed the frictional resistance created between the bottom of the block and the surface of the incline. Because many of the assumptions from this method were criticized by scientists over time, modified Newmark sliding block methods were proposed. As the original and modified Newmark sliding block methods were introduced, the need to account for the uncertainty in the way soil would behave under earthquake loading became a big challenge. Deterministic and probabilistic methods have been used to incorporate parameters that would account for some of the uncertainty in the analysis. In an attempt to use a probabilistic approach in understanding how slopes might fail, the Pacific Earthquake Engineering Research Center proposed a performance-based earthquake engineering framework that would allow decision-makers to use probabilistically generated information to make decisions based on acceptable risk. Previous researchers applied this framework to simplified Newmark sliding block models, but the approach is difficult for engineers to implement in practice because of the numerous probability calculations that are required. The work presented in this thesis provides a solution to the implementation of the performance-based approach by providing a simplified procedure for the performance-based determination of seismic slope displacements using the Rathje & Saygili (2009) and the Bray and Travasarou (2007) simplified Newmark sliding block models. This document also includes hazard parameter maps, which are an important part of the simplified procedure, for five states in the United States. A validation of the method is provided, as well as a comparison of the simplified method against other commonly used approaches such as deterministic and pseudo-probabilistic.
Power quality assessment using a robust nonlinear estimation technique

This paper describes a new two stage robust Newton type numerical algorithm for power quality assessment in electric power systems. In order to estimate current and voltage spectra simultaneously with the fundamental frequency, in the first algorithm stage, the robust Newton type algorithm is applied. In the second algorithm stage power quality indicators are calculated. Particularly the active, reactive, apparent and distortion powers are determined. As the main advantage, the technique provides estimates not sensitive to frequency deviations and bad data often appearing as a consequence of the communication error, incomplete measurement, errors in mathematical models etc. The algorithm performance is tested under laboratory conditions using the distorted voltage and current signals digitized during an AC-motor start.
MISD-SLAM: Multimodal Semantic SLAM for Dynamic Environments

Simultaneous localization and mapping (SLAM) is one of the most essential technologies for mobile robots. Although great progress has been made in the field of SLAM in recent years, there are a number of challenges for SLAM in dynamic environments and high-level semantic scenes. In this paper, we propose a novel multimodal semantic SLAM system (MISD-SLAM), which removes the dynamic objects in the environments and reconstructs the static background with semantic information. MISD-SLAM builds three main processes: instance segmentation, dynamic pixels removal, and semantic 3D map construction. An instance segmentation network is used to provide semantic knowledge of surrounding environments in instance level. The ORB features located on the predefined dynamic objects are removed directly. In this way, MISD-SLAM effectively reduces the impact of dynamic objects to provide precise pose estimation. Then, combining multiview geometry constraint with K -means clustering algorithm, our system removes the undefined but moving pixels. Meanwhile, a 3D dense point cloud map with semantic information is reconstructed, which recovers the static background without the corruptions of dynamic objects. Finally, we evaluate MISD-SLAM by comparing to ORB-SLAM3 and the state-of-the-art dynamic SLAM systems in TUM RGB-D datasets and real-world dynamic indoor environments. The results indicate that our method significantly improves the localization accuracy and system robustness, especially in high-dynamic environments.