Miracle-dz/UniFAR-FTUs huggingface dataset:
Food Flavor Perception as Expressed via Sensory Spectrograph

In our previous studies, we examined the relationship between changes in mood, verbal (semantic) behavior, and non-verbal (skin temperature) activity induced by inhalation of essential oil fragrances, as well as linalool and its enantiomers. Sensory evaluation was a key component of these studies. We have found that perceived sensory attributes reported by participants can be represented via sensory spectrograph: A bar graph where the mean of the impression is plotted against descriptors of the setting for the semantic impression. In this paper, we present our latest attempts at assessing the taste of food using a sensory spectrograph. We conducted two studies: One in which participants assessed the taste of cookies with or without bean curd lees and one in which participants evaluated the taste of Miso soup and Sumashi soup as a function of salty concentration and soup stock consistency.
Energy-efficient two-dimensional skyline query processing in wireless sensor networks

Wireless sensor networks have been widely used for environment monitoring and event surveillance. In recent years, the skyline query, as an important operator for multiple-criteria decision making, has received much attention because of its many real-world applications. In this paper, we propose a new filter-based method, called SKYFILTER, for skyline query processing. Our method provides an enhanced efficiency by reduction of the total wireless communication between sensor nodes. In addition, the proposed method also provides a better performance than other existing methods with respect to the computational efficiency of skyline computation in sensor nodes. In this paper, we first present a new multiple filter-based technique to reduce wireless communication. Then, we provide a new merge sort-based algorithm for efficient skyline computation in sensor nodes. We show through performance experiments and algorithm analysis that the proposed method generally provides a better performance than other existing methods in terms of the wireless communication cost and computational efficiency.
Estimation of Conditional Probabilities With Decision Trees and an Application to Fine-Grained POS Tagging

We present a HMM part-of-speech tagging method which is particularly suited for POS tagsets with a large number of fine-grained tags. It is based on three ideas: (1) splitting of the POS tags into attribute vectors and decomposition of the contextual POS probabilities of the HMM into a product of attribute probabilities, (2) estimation of the contextual probabilities with decision trees, and (3) use of high-order HMMs. In experiments on German and Czech data, our tagger outperformed state-of-the-art POS taggers.
Altered rapid eye movement sleep timing in serotonin transporter knockout mice

The monoamine neurotransmitter serotonin has long been implicated in development and maintenance of sleep patterns, yet the role of the serotonin transporter (SERT) in these processes has not been evaluated in detail. We report that genetically engineered SERT knockout mice exhibit more REM sleep (REMS) than wild type littermates (11 vs 7% of recording time under baseline conditions) and display more frequent REMS bouts that last longer. This phenotype resembles the previously reported long-term effect of repeated treatment with SERT inhibitor compounds rather than the acute REMS suppressing effect of treatment with such compounds, and is thus likely to reflect neuroadaptations to the absence of SERT, rather than an acute effect of its absence in the adult. While electroencephalographic (EEG) spectra did not differ between SERT knockout and wild type mice during non-REM sleep (NREMS) or REMS, the dynamics of the EEG during the transition from NREMS to REMS differed between the genotypes. The surge in EEG power in both the 6–9 Hz and 10–16 Hz ranges that occurs just prior to the onset of REMS (pre-REMS power surge) is of greater magnitude in SERT knockout mice than in wild type littermate controls. This observation contrasts with the reduced magnitude pre-REMS power surge observed in rats subjected to REMS deprivation relative to yoked controls. These results indicate that the pre-REMS power surge is influenced by REMS history and by monoaminergic transmission. Genetic differences in serotonin systems and developmental exposure to SERT blockers are likely to exert effects on REMS.
The Development of a Web-Based Meranaw Language Lexicon Using a Rule-Based Morphological Analyzer for Meranaw Verbs: Dindiyorobasa App

This study introduces the “Dindiyorobasa App”, a web-based Meranaw language lexicon featuring the Rule-Based Morphological Analyzer for Meranaw Verbs (rbMAM). The app addresses critical challenges in Meranaw word analysis, minimizing over-stemming and under-stemming issues. Evaluation through the System Usability Scale (SUS) reveals an impressive score of 87.4, indicating exceptional user satisfaction and usability. The Dindiyorobasa App, with its advanced morphological analyzer, not only contributes to the preservation and revitalization of the intricate Meranaw language but also enhances the capabilities of the Meranaw-English Language Translator (MELT). The study underscores the feasibility of web-based applications in linguistics and digital preservation, providing a valuable resource for Meranaw speakers, linguists, and language enthusiasts while setting the stage for future advancements in the field.
The impact of formal institutions on social trust formation: A social-cognitive approach

While formal institutions are recognized as having an effect on trust formation, no theoretical or empirical models exist to formalize this relationship. This study introduces a new conceptual framework to explain trust building by individuals and the role that formal rules and laws may play in this process. Drawing on a social-cognitive theory of psychology, we present trust as composed of personal, interpersonal, and intrapersonal components with the latter encompassing formal institutions. We further demonstrate that there are three mechanisms – sanction, legitimacy, and autonomy – through which formal institutions may affect trust levels either directly or indirectly. In addition, our empirical analysis furnishes evidence of heterogeneity in institutional effects on trust, suggesting that the autonomy dimension of the institutional framework is particularly important for trust formation processes.
Spatial Structure, Transmission Modes and the Evolution of Viral Exploitation Strategies

Spatial structure and local migration are predicted to promote the evolution of less aggressive host exploitation strategies in horizontally transmitted pathogens. Here we explore the effect of spatial structure on the evolution of pathogens that can use both horizontal and vertical routes of transmission. First, we analyse theoretically how vertical transmission can alter evolutionary trajectories and confirm that space can impede the spread of virulent pathogens. Second, we test this prediction using the latent phage λ which transmits horizontally and vertically in Escherichia coli populations. We show that the latent phage λ wins competition against the virulent mutant λcI857 in spatially structured epidemics, but loses when spatial structure is eroded. The vertical transmission of phage λ immunizes its local host pool against superinfection and prevents the spread of the virulent λcI857. This effect breaks down when mixing facilitates horizontal transmission to uninfected hosts. We thus confirm the importance of spatial structure for the evolutionary maintenance of prudent infection strategies in latent viruses.
Immersion and invariance stabilization of switched nonlinear systems under arbitrary switchings

Switched nonlinear systems are control systems that involve both continuous and discrete dynamics. In this paper, we consider the problem of global stabilization of switched nonlinear systems under arbitrary switchings. A nonconventional method of immersion and invariance (I&I) which does not require the Lyapunov function of the plant is extended to stabilization of general switched nonlinear systems. Then, we apply the proposed control strategy to switched nonlinear systems in feedback form. Finally, a numerical example is given to show the effectiveness of the proposed method.
City Research Online

The world is faced with a growing number of people who live with chronic medical conditions. There have been numerous digital interventions into personal management of these diseases in recent years, yet gaps remain in the HCI literature. In particular, we lack a systematic understanding of user requirements in tools that support independent management while away from external inﬂuences. This paper presents a ﬁrst investigation into low-intervention support for self-management. A mobile application enabled individuals to capture contextual information related to their health in the form of photographs. Through a month-long user study, we identify four management trends amongst our participants and describe their inﬂuence on mobile application adoption.
Prediction of Lift and Drag Coefficients for Aircrafts Based on CNN-ATT

The accurate establishment of the aerodynamic model of an aircraft is the basis for subsequent research on various aspects of the aircraft. Deep learning, with its powerful ability to fit nonlinear problems, has gradually become the focus of research on various difficult problems. In this paper, we propose a method for predicting the lift and drag coefficient of an aircraft based on a convolutional neural network. Our dataset is different from most wing aerodynamic parameter prediction datasets. Our dataset is unique in that it incorporates Mach and angle of attack information into images of the overall aircraft, with the lift coefficient and drag coefficient as the outputs. Our neural network is distinct from others in that it utilizes the final fully connected layer as the output layer for direct prediction of lift and drag coefficients in the regression task. Furthermore, we have incorporated an attention mechanism into our model to enhance its generalization ability and prevent it from being affected by noise or irrelevant information. We refer to this network as convolutional neural network with Attention mechanism(CNN-ATT). Our experiments demonstrate that our method outper-forms traditional approaches using vectors composed of aircraft structure and other information as inputs, as well as methods employing BP and LSTM networks, in terms of both information representation capability and prediction accuracy. Specifically, we have identified algorithmic and technical improvements that contribute to the superior performance of our model. We have also considered other factors that may affect model performance.
Integrative Analysis of Deep Sequencing Data Identifies Estrogen Receptor Early Response Genes and Links ATAD3B to Poor Survival in Breast Cancer

Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor (), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 early responsive genes and compared the biological functions of the genes having binding within 20 kb of the transcription start site (TSS) to genes without such binding site. Our results show that the non-genomic action of via the MAPK pathway, instead of direct binding, may be responsible for early cell responses to activation. Our results also indicate that the responsive genes triggered by the genomic pathway are transcribed faster than those without binding sites. The survival analysis of the 777 responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have binding site within 20 kb of its TSS, to be significantly associated with poor patient survival.
Mapping and characterization of the functional domains of the nucleolar protein RNA helicase II/Gu.

RNA helicase II/Gu (RH-II/Gu) is a nucleolar RNA helicase of the DEAD-box superfamily. In this study, the functional domains of RH-II/Gu molecule were mapped by fusing the protein or its deletion mutants with a green fluorescence protein and subsequently transfecting or microinjecting the recombinant constructs into HeLa cells. In addition to the identification of a nuclear localization signal (NLS) in the N-terminus and a nucleolar targeting signal in the central helicase domain, a hidden NLS and a nucleolar targeting signal were found in the C-terminal arginine/glycine-rich domain. RH-II/Gu colocalized with fibrillarin, a component of the dense fibrillar region of the nucleolus. Overexpression of the entire RH-II/Gu protein or specific domains of the protein in HeLa cells did not interfere with the normal distribution of fibrillarin. However, when the helicase domain was truncated, the distribution pattern of fibrillarin was distorted. Microinjection of the wild-type RH-II/Gu cDNA into the nucleus of HeLa cells did not disrupt normal cell growth. However, when cells were injected with mutant DNA, only a small percentage of HeLa cells progressed through the cell cycle. Analysis of centrosomes in transfected cells demonstrated that most of the mutant-expressing cells were arrested early in the cell cycle. The results suggest that each of the structural domains of RH-II/Gu is necessary for cell growth and cell cycle progression.
Photoconductive zinc oxide-composite paper by pilot paper machine manufacturing

Smart materials can be used for a wide variety of applications, including sensing and energy harvesting. Implementation of smart materials in large area devices requires scalable manufacturing. The use of paper-making techniques would offer an enormous production capacity, allowing for low-cost and large-scale manufacturing. In this work we present a successful pilot scale paper machine manufacturing of functional composite papers (100 m min−1 with a web width of 30 cm) based on cellulose fibres and commercial tetrapodal zinc oxide microwhiskers (ZnO-Ts). Carbon electrodes could successfully be printed on the paper to form complete electronic devices where the paper itself is the active material. This enabled development of a ZnO-composite paper photosensor, where we characterized its stability, sensitivity and speed. The devices show excellent photosensing properties over a wide range of light irradiances (0.01–1 Sun), including short response times (∼10 s) and long-term stability. Under simulated sunlight and a bias voltage of 1 V, small (0.5 cm2) two-probe interdigitated photosensor devices provided 12 μA photocurrent. Under the same conditions, four-probe measurements of the composite paper showed a sheet resistance of 6.9 · 107 Ω/sq. Four-probe measurements also demonstrated that the paper conductivity varies linearly with light irradiance. To the best of our knowledge, this is the first example of pilot paper machine production of an optoelectronic paper, demonstrating the potential for large-scale paper manufacturing of active smart paper from low-cost industrial bulk materials.
Influence of Stress and Antibiotic Resistance on Cell-Length Distribution in Mycobacterium tuberculosis Clinical Isolates

Mycobacterial cellular variations in growth and division increase heterogeneity in cell length, possibly contributing to cell-to-cell variation in host and antibiotic stress tolerance. This may be one of the factors influencing Mycobacterium tuberculosis persistence to antibiotics. Tuberculosis (TB) is a major public health problem in developing countries, antibiotic persistence, and emergence of antibiotic resistance further complicates this problem. We wanted to investigate the factors influencing cell-length distribution in clinical M. tuberculosis strains. In parallel we examined M. tuberculosis cell-length distribution in a large set of clinical strains (n = 158) from ex vivo sputum samples, in vitro macrophage models, and in vitro cultures. Our aim was to understand the influence of clinically relevant factors such as host stresses, M. tuberculosis lineages, antibiotic resistance, antibiotic concentrations, and disease severity on the cell size distribution in clinical M. tuberculosis strains. Increased cell size and cell-to-cell variation in cell length were associated with bacteria in sputum and infected macrophages rather than liquid culture. Multidrug-resistant (MDR) strains displayed increased cell length heterogeneity compared to sensitive strains in infected macrophages and also during growth under rifampicin (RIF) treatment. Importantly, increased cell length was also associated with pulmonary TB disease severity. Supporting these findings, individual host stresses, such as oxidative stress and iron deficiency, increased cell-length heterogeneity of M. tuberculosis strains. In addition we also observed synergism between host stress and RIF treatment in increasing cell length in MDR-TB strains. This study has identified some clinical factors contributing to cell-length heterogeneity in clinical M. tuberculosis strains. The role of these cellular adaptations to host and antibiotic tolerance needs further investigation.
Maximum coverage and maximum connected covering in social networks with partial topology information

Viral marketing campaigns seek to recruit the most influential individuals to cover the largest target audience. This can be modeled as the well-studied maximum coverage problem. Another related problem, called the maximum connected cover, is when recruited nodes have to be connected. This problem ensures a strong coordination among the influential nodes which are the backbone of the marketing campaign. In this work, we are interested on both of these problems. Most of the related literature assumes knowledge about the topology of the network. Even in that case, the problem is known to be NP-hard. We analyse heuristics to these models assuming different knowledge levels about the topology of the network. We quantify the difference between these heuristics and the local and global greedy algorithms.
Post Flood Damage Assessment Using Aerial Imagery

Visual Scene understanding is one of the most efficient methods to assess the damage caused by natural disasters. Many datasets exist that contain aerial images from satellites or UAVs. Floodnet is one of those datasets containing images useful to assess the damage done by a flood. Different network architectures have been developed for classification and semantic segmentation using supervised learning to achieve reasonable accuracy. However, they cannot address the problem of considerable class imbalance in the Floodnet dataset. This imbalance arises due to the few labeled images, as labeling an aerial image is an expensive and high-effort task. This imbalance in classes prevents the model from achieving good accuracy. This work uses a semi-supervised learning approach to address the classification and semantic segmentation problem. The classical Pseudo Labeling approach has been implemented on the best-performing model in classification and semantic segmentation objectives. Pseudo-labels are assigned to unlabeled images by predicting classes from labeled images based on the probability distribution. These pseudo-labels are then trained with ground-truth data to achieve better results. Classification models train themselves in a semi-supervised manner based on the pseudo-labels' generation algorithm. Semantic Segmentation models also generate pseudo-label masks for pixel-level identification. Classification model Resnet50 trained in a semi-supervised manner achieves accuracy and Fl-score of 94.97% and 0.971, respectively. Semantic Segmentation model Segformer achieves a Mean Intersection over Union (MIoU) score of 0.596.
Chance-Constrained Programs for Link Prediction

In this paper, we consider the link prediction problem, wher e we are given a partial snapshot of a network at some time and the goal is to predi ct a ditional links at a later time. The accuracy of the current prediction metho ds is quite low due to the extremeclass skew and the large number of potential links. In this paper, we describe learning algorithms based on chance constraine d programs and show that they exhibit all the properties needed for a good link pr edictor, namely, allow preferential bias to positive or negative class; handle skewness in the data; and scale to large networks. Our experimental results on three real-w or d coauthorship networks show significant improvement in predic tion accuracy over baseline algorithms.
Impact on rates and time to first central vascular-associated bloodstream infection when switching from open to closed intravenous infusion containers in a hospital setting

SUMMARY An open-label, prospective cohort, active healthcare-associated infection surveillance sequential study was conducted in four Italian intensive-care units. The aim was to determine the effect of switching from open (glass) to closed fully collapsible plastic intravenous (i.v.) infusion containers (Viaflo®) on rate and time to onset of central venous catheter-associated bloodstream infections (CVC-BSI). A total of 1173 adult patients were enrolled. The CVC-BSI rate during the open container period was significantly higher than during the closed container period (8·2 vs. 3·5 BSI/1000 CVC days, relative risk 0·43, 95% confidence interval 0·22–0·84, P=0·01). The probability of developing a CVC-BSI was assessed over time comparing open and closed i.v. infusion containers. In the closed container period, it remained fairly constant (0·8% at days 1–3 to 1·4% at days 7–9) whereas during the open container period it increased (2% at days 1–3 to 5·8% at days 7–9). Overall, the chance of acquiring a CVC-BSI significantly decreased by 61% in the closed container period (Cox proportional hazard ratio 0·39, P=0·004).
Poly(L-lactide) crystallization topography directs MC3T3-E1 cells response

Abstract Biomaterial surface topography significantly influences cellular form and function. Using poly(L-lactic acid) films with normal spherulites, banded spherulites, and amorphous surfaces as model substrates, we conducted a systematic assessment of the role for polymer crystallization induced surface morphologies on cell growth and contact guidance. Microscopy and image analysis showed that the MC3T3-E1 cells spread out in a random fashion on the amorphous substrate. At 24 h post-seeding, MC3T3-E1 cells on both types of spherulite surfaces were elongated and aligned along the spherulite radius direction. For the banded spherulite surface with radial stripes and coupling annular grooves, the cell orientation and cell nuclear localization were related to the grooves structure. With increasing time, this orientation preference was weaker. These results demonstrate that the patterning of polymer crystallization structure provide important signals for guiding cells to exhibit characteristic orientation and morphology especially in the early stages of regeneration.
Low-discrepancy sets for high-dimensional rectangles : a survey

A sub-area of discrepancy theory that has received much attention in computer science recently, is that of explicit constructions of low-discrepancy point sets for various types of rectangle families in high dimension. This research has led to interesting applications in error-control coding, distributed protocols, Web document filtering, derandomization, and other areas. We give a short survey of this area here.