Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Control of an Ornithopter for Non-Equilibrium Maneuvers

Flapping-winged flight is very complex, and it is difficult to efficiently model the unsteady airflow and nonlinear dynamics for online control. While steady state flight is well understood, transitions between flight regimes are not readily modeled or controlled. Maneuverability in non-equilibrium flight, which birds and insects readily exhibit in nature, is necessary to operate in the types of cluttered environments that small-scale flapping-winged robots are best suited for. The advantages of flapping wings over quadrotors and fixed-wing fliers are realized in the ability to transition from forward flight to hover to gliding. Flight in the transitions between these regimes necessitates the development of novel modeling techniques and online control techniques to accurately complete these types of maneuvers.In this thesis, methods for modeling and controlling the transitions between takeoff and diving maneuvers are developed for a flapping-winged micro aerial vehicle (MAV), the H2Bird. To transition into takeoff and steady state flight, a cooperative launching system is developed for the H2Bird by carrying it on the back of a 32 gram hexapedal millirobot, the VelociRoACH. The necessary initial velocity and pitch angle are determined for take off using force data collected in a wind tunnel, and the VelociRoACH is used to reach these initial conditions for successful launch. The models for the diving maneuver are generated using an automatic piece-wise affine identification technique. The flight conditions during the maneuver are segmented into separate regions and least-squares is used to estimate affine linear models for each modeling region. These models are used to compute the reachability sets for the recovery conditions for safe diving, and linear quadratic regulator controllers are used to maintain stable conditions before and after the dive. The data-driven automatic modeling techniques and controller design processes can be extended to additional flight maneuvers.
Relationships between Lower Extremity Musculoskeletal Disorders Symptoms and Tibial Acceleration Measures in Distribution Center Workers

In a survey of 341 workers, we have found lower extremity musculoskeletal symptoms to be prevalent in distribution center employees working in material handling jobs. This study was a cross-sectional field study aimed at developing risk models showing associations between tibial acceleration and lower extremity musculoskeletal disorder symptoms. One hundred thirty two participants volunteered to wear uni-axial accelerometers that quantified their bilateral tibial acceleration exposures during two hours of normal work activities and also completed a questionnaire assessing individual factors and their experience with lower extremity musculoskeletal symptoms. The questionnaire and accelerometer data were used to develop logistic regression models exploring the relationships between the likelihood of self-reported lower extremity symptoms in the hip/thigh, knee, and ankles/feet and relevant biomechanical and individual exposure variables. An outcome score was created by multiplying the symptom frequency score by the symptom severity score by the therapy score for both the knees and the ankles/feet regions. Only the symptom frequency and severity scores were multiplied to create the hip/thigh outcome score. Multiple logistic regression models were used to predict the probability of being symptomatic based on the accelerometer, work exposure, and individual characteristics predictor variables. Table 1 shows the sensitivity of the models predicting symptoms in the hip/thighs, knees, and ankles/feet and the contributing predictor variables.
Transient Stability Prediction of Power Systems Using Post-disturbance Rotor Angle Trajectory Cluster Features

Abstract A machine learning-based approach is proposed to predict the transient stability of power systems after a large disturbance. The post-disturbance trajectories of generator rotor angles are taken as a whole cluster, and 19 cluster features are defined to depict the overall transient stability characteristics of the power systems. A hybrid approach, which combines the linear support vector machine with the decision tree, is proposed to generate the final transient stability classifier. Comprehensive studies are conducted on the IEEE 39-bus and IEEE 145-bus test systems to verify the performance of the proposed approach. Test results show that by using the cluster features and the proposed approach, the transient stability of the power system can be predicted accurately with a shorter training time. Furthermore, the prediction classifier is robust to unknown load levels and network topologies, especially under situations when some generator measurements are unavailable and the number of input cluster features is independent of the system scale, making the proposed approach more suitable to transient stability prediction of large-scale power systems.
Ca -activated K channel inhibition by reactive oxygen species

channel inhibition by reactive oxygen species. Am J Physiol Cell Physiol 282: C461–C471, 2002. First published September 21, 2001; studied the effect of H 2 O 2 on the gating behavior of large-conductance Ca 2 (cid:1) -sensitive voltage-dependent K (cid:1) (K V,Ca ) channels. We recorded potassium currents from single skeletal muscle channels incorporated into bilayers or using macropatches of Xenopus laevis oocytes membranes expressing the human Slowpoke (h Slo ) (cid:2) -subunit. Exposure of the intracellular side of K V,Ca channels to H 2 O 2 (4–23 mM) leads to a time-depen- dent decrease of the open probability ( P o ) without affecting the unitary conductance. H 2 O 2 did not affect channel activity when added to the extracellular side. These results provide evidence for an intracellular site(s) of H 2 O 2 action. Desferri- oxamine (60 (cid:3) M) and cysteine (1 mM) completely inhibited the effect of H 2 O 2 , indicating that the decrease in P o was mediated by hydroxyl radicals. The reducing agent dithiothreitol (DTT) could not fully reverse the effect of H 2 O 2 . However, DTT did completely reverse the decrease in P o induced by the oxidizing agent 5,5 (cid:4) -dithio-bis-(2-nitrobenzoic acid). The incomplete recovery of K V,Ca channel activity pro- moted by DTT suggests that H 2 O 2 treatment must be modi-fying other amino acid residues, e.g., as methionine or tryptophan, besides cysteine. Noise analysis of macroscopic currents in Xenopus oocytes expressing h Slo channels showed that H 2 O 2 induced a decrease in current mediated by a decrease both in the number of active channels and P o . K V,Ca channels; H 2 O 2 n -decane were purchased from Sigma (St. Louis, MO). The perhydrol 30% of hydrogen peroxide, chloroform, ethanol, and methanol were purchased from Merck Chemical.
The Ca 2 (cid:1) Homeostasis Defects in a pgm2 (cid:2) Strain of Saccharomyces cerevisiae Are Caused by Excessive Vacuolar Ca 2 (cid:1) Uptake Mediated by the Ca 2 (cid:1) -ATPase Pmc1p*

, Loss of the major isoform of phosphoglucomutase (PGM) causes an accumulation of glucose 1-phosphate when yeast cells are grown with galactose as the carbon and energy source. Remarkably, the pgm2 (cid:2) strain also exhibits a severe imbalance in intracellular Ca 2 (cid:1) homeostasis when grown under these conditions. In the present study, we examined how the pgm2 (cid:2) mutation alters yeast Ca 2 (cid:1) homeostasis in greater detail. We found that a shift from glucose to galactose as the carbon source resulted in a 2-fold increase in the rate of cellular Ca 2 (cid:1) uptake in wild-type cells, whereas Ca 2 (cid:1) uptake increased 8-fold in the pgm2 (cid:2) mutant. Disruption of the PMC1 gene, which encodes the vacuolar Ca 2 (cid:1) -ATPase Pmc1p, suppressed the Ca 2 (cid:1) -related phenotypes observed in the pgm2 (cid:2) strain. This suggests that excessive vacuolar Ca 2 (cid:1) uptake is tightly coupled to these defects in Ca 2 (cid:1) homeostasis. An in vitro assay designed to
A D ATA M INING A PPROACH FOR F ILTERING O UT S OCIAL S PAMMERS IN L ARGE -S CALE T WITTER D ATA C OLLECTIONS

Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter draws many spammers or other fake accounts to post malicious links and infiltrate legitimate users' accounts with many spam messages. Therefore, it is crucial to recognize and screen spam tweets and spam accounts. As a result, spam detection is highly needed but still a difficult challenge. This article applied several Bio-inspired optimization algorithms to reduce the features' dimensions in the first stage. Then we used several classification schemes in the second stage to enhance the spam detection rate in three real Twitter data collections. The performance of the chosen classifiers also revealed that Random Forest and C4.5 classifiers achieved the highest Accuracy, Precision, Recall, and F1-score even on class imbalance.
BERT-ER: Query-specific BERT Entity Representations for Entity Ranking

Entity-oriented search systems often learn vector representations of entities via the introductory paragraph from the Wikipedia page of the entity. As such representations are the same for every query, our hypothesis is that the representations are not ideal for IR tasks. In this work, we present BERT Entity Representations (BERT-ER) which are query-specific vector representations of entities obtained from text that describes how an entity is relevant for a query. Using BERT-ER in a downstream entity ranking system, we achieve a performance improvement of 13-42% (Mean Average Precision) over a system that uses the BERT embedding of the introductory paragraph from Wikipedia on two large-scale test collections. Our approach also outperforms entity ranking systems using entity embeddings from Wikipedia2Vec, ERNIE, and E-BERT. We show that our entity ranking system using BERT-ER can increase precision at the top of the ranking by promoting relevant entities to the top. With this work, we release our BERT models and query-specific entity embeddings fine-tuned for the entity ranking task.
Tumour-Associated Tissue Factor (TF)-mRNA Is A Precursor for Rapid TF-Microvesicle Release and A Potential Predictive Marker for the Risk of Pulmonary Embolism (PE) in Gastrointestinal Cancer Patients

Anthony Tumour-associated tissue factor (TF)-mRNA is a precursor for rapid TF-microvesicle release and a potential predictive marker for the risk of pulmonary embolism (PE) in gastrointestinal cancer patients. Journal of Biotechnology and Biomedicine (2022): 78-93. Abstract Predicting which cancer patients are at risk of thrombosis remains a key challenge to effective thromboprophylaxis. This study was based on the hypothesis that the rapid release of TF-containing MV occurs in cancer cells that possess high levels of TF mRNA, permitting the transient but amplified TF-protein production in response to cellular activation. To gather preliminary clinical evidence for this hypothesis the correlation between the levels of tumour-associated and further supports the hypothesis that the process is driven by the rapid translation of mRNA into TF-protein, following stimulation.
Design of a low-power VLSI temperature sensor

A low power CMOS temperature sensor with digital output is presented in this work. The sensor is current based, the combination of dependent and independent current that control the frequency of 2 ring oscillators. The difference in frequency of this two ring oscillator is converted into a digital temperature code by asynchronous counters. The reference current and the PTAT current is generated by applying a temperature dependent voltage across two resistors with different temperature coefficients. To achieve a low power consumption, the temperature voltage reference operates under 200 mV and resistors in the Mega Ohms range are utilized. In order to save area, an additional current mirror is used to reduce the current without increasing the resistor’s value. This sensor was implemented in TSMC 180nm technology and occupies an area of 0.085mm2. After post layout simulations, the sensor shows a current consumption of 1. 7μA, a resolution of 0.17oC in the 0-60oC temperature range with a conversion time of 1.8ms and 3.2 nJ of energy per conversion.
ActivePDB: Active Probabilistic Databases

We present a novel framework for uncertain data management, called ActivePDB. We are given a relational probabilistic database, where each tuple is correct with some probability; e.g., a database constructed from textual data using information extraction. We are now given a query and we want to determine the correctness of its results. Unlike probabilistic databases, we have an oracle that can resolve the uncertainty, such as a domain expert that can verify data against their sources. Since verification may be costly, our goal is to determine the correct output of the query, while asking the oracle to verify as few tuples as possible. ActivePDB provides an end-to-end solution to this problem. In a nutshell, we first track provenance to identify which input tuples contribute to the derivation of each output tuple, and in what ways. We then design an active learning solution to iteratively choose tuples to be verified based on the provenance structure and on an evolving estimation of the probability of the tuples correctness. We will demonstrate ActivePDB in the context of the NELL database of extracted facts, allowing participants to both pose queries and play the role of oracles.
Of Course it's Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization

Recent developments in critical information visualization have brought the field's attention to political, feminist, ethical, and rhetorical aspects of data visualization. However, less work has explored the interplay between design decisions and political ramifications—structures of authority, means of representation, etc. In this paper, we build upon these critical perspectives and highlight the political aspect of civic text visualization especially in the context of democratic decision‐making. Based on a critical analysis of survey papers about text visualization in general, followed by a review on the status quo of text visualization in civics, we argue that civic text visualization inherits an exclusively analytic framing. This framing leads to a series of issues and challenges in the fundamentally political context of civics, such as misinterpretation of data, missing minority voices, and excluding the public from decision making processes. To span this gap between political context and analytic framing, we provide a series of two‐pole conceptual dimensions, such as from singular user to multiple relationships, and from complexity to inclusivity of visualization design. For each dimension, we discuss how the tensions between these poles can help surface the political ramifications of design decisions in civic text visualization. These dimensions can thus help visualization researchers, designers, and practitioners attend more intentionally to these political aspects and inspire their design choices. We conclude by suggesting that these dimensions may be useful for visualization design across a variety of application domains, beyond civic text visualization.
Learning iterative optimisation for deformable image registration with recurrent convolutional networks

We propose a fully deep learning-based approach for de-formable image registration, that aims to emulate the structure of gradient-based optimisation as used in conventional registration and thus learns how to optimise. Our architecture consists of recurrent updates on a convolutional network with deep supervision and uses dynamic sampling of the cost function and hidden states to mimic gradient-based optimization, requiring fewer iterations than traditional techniques. Without pre-registration, our method achieves 2 mm TRE on the DIR-Lab COPD dataset and outperforms Adam optimisation. Our code is publicly available at: https://anonymous.4open. science/r/Learn2Optimise-BF1F/.
Plasma lncRNA profiling identified BC200 and NEAT1 lncRNAs as potential blood-based biomarkers for late-onset Alzheimer’s disease

Long non-coding RNAs (lncRNA) play critical roles in pathogenesis of neurodegenerative diseases. Human plasma carries lncRNAs that are stable in the blood, and their disease-specific profile have made them valuable biomarkers for some diseases. This study reports screening of the plasma levels of 90 lncRNAs in patients with Alzheimer disease (AD) to find out plasma-based AD biomarkers. Total RNA was isolated from plasma samples of 50 AD and 50 matched healthy controls. The plasma samples of 10 advanced AD patients and 10 matched healthy controls were screened for expression levels of 90 lncRNAs using Human LncRNA Profiler qPCR Array Kit (SBI). Based on the profiling results, lncRNAs BC200, NDM29, NEAT1, FAS-AS1 and GAS5-AS1 were selected for further analysis in all samples and their biomarker potency was evaluated by ROC curve analysis. We further surveyed RNAseq data by in silico analysis. We found that the NEAT1 and BC200 levels in the plasma of the AD patients were significantly higher compared with the control group (P=0.0021, p= 0.02, respectively). ROC curve analysis showed that the plasma level of NEAT1 and BC200 discriminated AD patients from healthy controls with sensitivity of 72 % and 60 %, and specificity of 84 % and 91 % respectively. Moreover, NEAT1 discriminated MCI (60 % sensitivity and 91 % specificity) and advanced-AD patients from healthy controls (73 % sensitivity and 71 % specificity). Besides, plasma level of BC200 discriminated the pre-clinical subjects from healthy controls with 83 % sensitivity and 66 % specificity. A positive correlation was also observed between plasma levels of BC200 with the age patients (r = 0.34, p=0.02). In silico RNAseq data analysis showed that a total of 33 lncRNAs were up-regulated but 13 lncRNAs were down-regulated significantly in AD patients compared with the healthy controls. In conclusion, this study elucidated that the plasma levels of lncRNAs NEAT1 and BC200 might be considered as potential blood-based biomarkers for AD development and progression.
Molecular Dynamics and MM-PBSA Studies for Deciphering Molecular Interactions of Valproic Acid with CYP2C9 Mutants F114L and I207T

: Valproic Acid (VPA) is a widely used drug, particularly in neuropsychiatric disorders, while showing promise in other types of diseases such as cancer. VPA metabolism via cytochrome P450 (CYP) pathway is responsible from only ~10% of the total drug dose. However, due to high risk of severe adverse reactions in liver and pancreas, interaction of VPA with CYP2C9 remains to be delineated chiefly in CYP2C9 mutants. Hence, here we implemented a molecular dynamics study, followed by MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method based relative binding energy estimation to understand how F114L and I207T CYP2C9 mutants changed their binding mode towards VPA in comparison to wild type (WT) CYP2C9. Results indicated that while F114L and I207T mutants have showed significant decrease in total relative binding energy, compared with WT, there were a clear shift of occupied amino acids responsible for VPA interaction in mutants vs WT. Overall, here for the first time in literature, this novel shift of VPA interacting amino acids in F114L and I207T mutants were reported. Limitations and future perspective of the data were also discussed.
Systematic identification of tRNA genes in Drosophila melanogaster

Transfer RNAs (tRNAs) are ubiquitous adapter molecules that link specific codons in messenger RNA (mRNA) with their corresponding amino acids during protein synthesis. The tRNA genes of Drosophila have been investigated for over half a century but have lacked systematic identification and nomenclature. Here, we review and integrate data within FlyBase and the Genomic tRNA Database (GtRNAdb) to identify the full complement of tRNA genes in the D. melanogaster nuclear and mitochondrial genomes. We apply a logical and informative nomenclature to all tRNA genes, and provide an overview of their characteristics and genomic features.
Chimney ﬁre prediction based on environmental variables

Fire prediction and prevention is closely related to public security. In this paper, we propose a chimney ﬁre prediction model using data collected by the Twente Fire Brigade. Random forests are used to select explanatory variables from a large set of candidates. Furthermore, we build a nested Poisson generalized linear model based on house types and weather variables. Plausible predictions are obtained, that capture the salient spatial and temporal patterns seen in the data.
The flagellar anti- (cid:115) factor FlgM actively dissociates Salmonella typhimurium (cid:115) 28 RNA polymerase holoenzyme

The anti- (cid:115) factor FlgM of Salmonella typhimurium inhibits transcription of class 3 flagellar genes through a direct interaction with the flagellar-specific (cid:115) factor, (cid:115) 28 . FlgM is believed to prevent RNA polymerase (RNAP) holoenzyme formation by sequestering free (cid:115) 28 . We have analyzed FlgM-mediated inhibition of (cid:115) 28 activity in vitro. FlgM is able to inhibit (cid:115) 28 activity even when (cid:115) 28 is first allowed to associate with core RNAP. Surface plasmon resonance (SPR) was used to evaluate the interaction between FlgM and both (cid:115) 28 and (cid:115) 28 holoenzyme (E (cid:115) 28 ). The K d of the (cid:115) 28 –FlgM complex is ~ 2 × 10 −10 M ; missense mutations in FlgM that cause a defect in (cid:115) 28 inhibition in vivo increase the K d of this interaction by 4- to 10-fold. SPR measurements of E (cid:115) 28 dissociation in the presence of FlgM indicate that FlgM destabilizes E (cid:115) 28 , presumably via an interaction with the (cid:115) subunit. Our data provide the first direct evidence of an interaction between FlgM and E (cid:115) 28 . We propose that this secondary activity of FlgM, which we term holoenzyme destabilization, enhances the sensitivity of the cell to changes in FlgM levels during flagellar biogenesis.
IndividualsVariants in Convalescent and Community mRNA-Vaccinated Coronavirus 2 IgG Antibody Affinity to Omicron and Delta Variability in Severe Acute Respiratory Syndrome

The emergence of the omicron and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has begun a number of discussions regarding breakthrough infection, waning immunity, need and timing for vaccine boosters, and whether existing mRNA vaccines for the original SARS-CoV-2 strain are adequate. Our work leverages a biosensor-based technique to evaluate the binding efficacy of SARS-CoV-2 S1-specific salivary Abs to the omicron and delta variants using a cohort of mRNA-vaccinated ( n 5 109) and convalescent ( n 5 19) subjects. We discovered a wide range of binding efficacies to the variant strains, with a mean reduction of 60.5, 26.7, and 14.7% in measurable signal to the omicron strain and 13.4, 2.4, and 2 6.4% mean reduction to the delta variant for convalescent, Pfizer-, and Moderna-vaccinated groups, respectively. This assay may be an important tool in determining susceptibility to infection or need for booster immunization as the pandemic evolves. ImmunoHorizons , 2022, 6: 307 – 311.
NMR Study of the p-DNA Duplex [(4 1 ~21)-31-Desoxyribopyranosyl-(m5CGDDTTm5CG)2] and Comparison with its p-RNA Analogue

: The determination of the solution structure of small non-natural oligopeptides and oligonucleotides by NMR, which is one of the main research topics of our group, is illustrated on the example of an B-mer p-DNA duplex. p-DNA, the 3'-desoxy analogue of p-RNA, forms an highly selective pairing system but its pairing strength is less than that of analogous p-RNA sequences. The NMR study reveals that the backbone of p-DNA corresponds more closely to the conformation predicted for pentapyranose nucleic acids by qualitative conformational analysis than p-RNA.