Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Resistin Regulates Immunity to Helminths and Sepsis

Author(s): Jang, Jessica | Advisor(s): Nair, Meera G | Abstract: Resistin-like molecules (RELM), a family of mammalian-conserved secreted proteins, have immunoregulatory effects in helminth infection, acting as a negative feedback loop to limit excess inflammation at the expense of increased parasite burden. This is through downregulation of the Th2 cytokines, IL-4 and IL-13, that are essential for alternative activation of macrophages and clearance of helminths. However, it is unclear how the human RELM proteins regulate immunity to helminth infections and inflammation. In this thesis, we have combine data from human studies and a transgenic mouse model, where human resistin and its regulatory region was inserted in mice (hRetnTg+). Human patients infected with filarial nematodes or soil-transmitted helminths had higher levels of circulating hResistin, and hResistin was associated with higher parasite burden. To assess whether hResistin directly increases helminth infection, we infected hRetnTg+ mice with the murine hookworm, Nippostrongylus brasiliensis (Nb), which revealed that hResistin is upregulated by macrophages and we identified IL-4/STAT6 signaling as a novel pathway for hResistin expression. Human resistin expression during helminth infection contributes to increased parasite burden and prolonged infection. RNA-seq of whole lung RNA demonstrated that hRetnTg+ mice had increase proinflammatory cytokines and toll-like receptor (TLR) signaling. In human patients infected with helminths, hResistin is correlated with proinflammatory cytokines, suggesting that hResistin contributes to increased helminth infection through the upregulation of Th1 cytokines. Structural interaction predictions revealed that hResistin binds to TLR4 in the same binding pocket as MD-2, the adaptor protein that mediates lipopolysaccharide(LPS)-TLR4 interaction. Given that LPS-TLR4 interaction initiates septic shock, we hypothesized that hResistin may protect the host from endotoxic shock by outcompeting LPS binding to TLR4 and blocking subsequent proinflammatory cytokine production. Competitive cell binding assays confirmed that hResistin does indeed outcompete LPS for binding to TLR4, and thereby decreases Th1 cytokines in human PBMC. In a mouse model of LPS-induced sepsis, hResistin expression or exogenous administration resulted in protection against endotoxic shock, which was associated with a decrease in proinflammatory cytokines that promote sepsis progression. Finally, we show that Nb-induced hResistin reduces mortality from sepsis. In summary, we have identified hResistin as a key regulator of inflammatory responses in helminth infections and sepsis.
Applying Mantel’s spatial method to the case of restaurants

Most conventional spatial analysis approaches that are used to study the effects of interpersonal spacing are positioned on urban geographical scales and are, therefore, applicable to open spaces. This article presents the challenges and limitations of the Mantel method, which is particularly appropriate for co-presence situations in enclosed spaces, and also shows how it is implemented. Traditionally used in biostatistics and ecology, the method is deployed here for the first time in the field of consumer behaviour. When applied to the case of restaurants, it helps to identify the effects of (here, socio-spatial) distance on the greater or lesser similarity of choices between customer groups and their spending. Because it allows a high degree of use across disciplines, particularly in management sciences, the Mantel method may interest practitioners seeking to (re-)arrange shared spaces, such as workspaces, shows, aeroplane areas, spas or condominium meetings.
Host suitability of Trichoplusia ni and Chrysodeixis chalcites (Lepidoptera: Noctuidae) for native and nonnative parasitoids expanding their host range

Abstract We evaluated the host suitability and related traits of Trichoplusia ni Hübner (Lepidoptera: Noctuidae) and Chrysodeixis chalcites Esper (Lepidoptera: Noctuidae), which is nonnative in North America, for the native parasitoids Campoletis sonorensis Cameron (Hymenoptera: Ichneumonidae) and Copidosoma floridanum Ashmead (Hymenoptera: Encyrtidae), and the nonnative parasitoid Cotesia vanessae Reinhard (Hymenoptera: Braconidae). For the larval parasitoid C. sonorensis and C. vanessae trials, three-day-old larvae of both hosts were used, whereas one-day-old eggs of both hosts were used for the egg–larval parasitoid C. floridanum trial. For suitability parameters on each host exposed separately to each of the three parasitoid species, we measured parasitoid emergence (parasitoid success), parasitoids that did not emerge (parasitoid cocoon mortality), the proportion of male offspring (parasitoid sex ratio), hosts that developed into moths (host success), hosts that died without developing into moths or producing a parasitoid (host mortality), parasitoids emerging from cocoon masses (brood size), and the developmental times of parasitoids and hosts. For C. sonorensis, the native host and the nonnative host were found to be similarly suitable. For C. vanessae, the native host was more suitable than the nonnative host. For C. floridanum, the native host was suitable, whereas the nonnative host was not; however, sublethal effects on both the native and nonnative hosts were observed. The differential suitability of the hosts observed in this study contributes to the understanding of this measure as a dynamic factor in the expansion of parasitoids into novel host species.
Phenological changes offset the warming effects on biomass production in an alpine meadow on the Qinghai–Tibetan Plateau

Phenology is an important indicator of plant responses to environmental changes and is closely correlated with biomass production. However, how changes in phenological events affect plant biomass production when exposed to changing temperature and precipitation remain unclear. We conducted a 4‐year manipulative experiment of warming and precipitation addition to explore phenology–biomass interactions under climate change in a dry alpine meadow on the central Qinghai–Tibetan Plateau from 2015 to 2018. In dry and warm years, warming delayed phenology and precipitation addition advanced them. Warming decreased the biomass of Kobresia pygmaea in 2018 and the biomass of Poa pratensis in 2015, 2017 and 2018. However, precipitation addition significantly increased the biomass of Poa pratensis and Potentilla multifida in most of the experimental years. Phenological changes regulated the responses of biomass to treatments. Specifically, delay of green up of P. pratensis and delay of withering of K. pygmaea induced by warming can increase biomass production, but it can be offset by the direct negative effects of warming on biomass. Synthesis. Here we show how warming‐induced drought tend to decrease the biomass production of graminoids and the negative effects of warming on the biomass of P. pratensis and K. pygmaea were partially offset by green up postponement and withering postponement respectively. Our results highlights phenology is a crucial regulator for biomass production under climate change. Hence, both direct and indirect effects of warming and precipitation addition on phenology and biomass cannot be ignored when predicting biomass responses to climate change.
Impact of personality traits and university green entrepreneurial support on students' green entrepreneurial intentions: the moderating role of environmental values

PurposeThe earth needs to go green as it clarifies that humans should adopt a sustainable lifestyle that will be friendly to the environment and society. The emerging stream of academic literature on “go green” adds a new perspective to the deep-rooted conception of entrepreneurship. The objective of green entrepreneurship is to follow the traditional concepts of entrepreneurship but also brings the additional potential for society and the environment. The present paper is designed to study the impact of personality traits and university green entrepreneurial support on green entrepreneurial intentions along with the moderating role of environmental values.Design/methodology/approachThe data are collected by using the survey method, and the five-point Likert scale is used for this purpose. The statistical techniques applied to the dataset were confirmatory factor analysis and partial least square structural equation modeling.FindingsThe results revealed that personality traits are positively and significantly associated with green entrepreneurial intention, but risk-averse students depict insignificant results towards green entrepreneurial intention. Also, university green entrepreneurial support results in positive green entrepreneurial intention. Environmental values also strengthen the association.Originality/valuePreviously, researchers emphasized the entrepreneurial intentions for the sake of improving the economy, decreasing unemployment and promoting small ventures, but none focused on the green aspects of entrepreneurship. To date, we still lack knowledge regarding students' green entrepreneurial intentions and how personality traits and green support affect the intention. The focus of the present study is to sustain nature and ecosystems through green entrepreneurship by providing directions and economic and noneconomic gains for investors, entrepreneurs, students, universities and societies.
The haunting specter of retro consumption

We propose to intensify theorizing on retromarketing and nostalgic consumption by further developing “hauntology” as a conceptual lens for assessing the retro aesthetic as a commodified affective excess of meaning. This allows us to explore the consumption of marketized retrospective signs not from the perspective of personal experiences or creative meaning-makings but rather as affective encounters that desire in consumption desperately latches onto. In our view, it is thus not an aesthetic satisfaction, nostalgic comfort, or playful emancipations that are offered to us by retro consumption. Following a darker development of hauntology, we find ourselves instead thrust into spectral presences that we can never quite articulate, a haunting within us in an atmosphere of late capitalism where temporal belief in the future has been “cancelled. ”
Triple novelty block detection and classification approach for lung tumor analysis

Recently the increased utilization of computer‐aided detection is more helpful to assist the radiologist, recognize the minute lung lesion. In this research article, we propose three novel block detection methods for lung lesion segmentation. First, an automatic algorithm for lung lesion detection and segmentation by using histogram‐based affine‐invariant detection (HAID) is proposed. HAID is a novel segmentation technique that applies enhanced maximally stable extremal region algorithm results to segment the lung lesion. The lung lesions are segmented with an accuracy of 91.5% and false positives per scan is 5.52. To reduce the false positives per scan of lung tumor segmentation, a spatial constraint‐based new fuzzy clustering (SCNFC) approach is then proposed. The experimental results proved the efficiency of the SCNFC technique in the successful segmentation of lung tumor tissues with high accuracy and reduced the false positive per scan (95.21% and 3.69 for a database of simulated computed tomography images). Finally, firefly search‐based Macqueen's K‐means clustering algorithm (FSMKC) is proposed. The performance of the FSMKC method is evaluated on series assessments using the Lung Image Database Consortium dataset (1505 images) and it achieved the accuracy of 97.18% and 96.28% of sensitivity with only 2.27 false positives per scan.
Adiponectin and leptin levels in migraineurs: Results from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

Background: Relationships of adipokines (ADP) with migraine are not well-established. We examined the relationship of adiponectin and leptin with migraine by aura symptoms. Methods: In a baseline cross-sectional data of Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), associations of ADP levels and migraine were assessed in a sample of 257 nondiabetic subjects, free from cardiovascular disease. Associations of ADP tertiles (dependent variable) and migraine status were tested using logistic regression models. Categories of migraine were created as follows: no headache (reference), migraine with aura (MA), and migraine without aura (MO) in all sample and by sex. Odds ratio (OR) with respective 95% confidence interval (CI) adjusted for age, sex, body mass index, and metabolic syndrome. Results: Among participants (46 years ± SD: 4.8), 47.5% were women and 36.2% had migraine (16.7% MA). Median values of leptin (ng/mL) increased gradually across subgroup: no headache: 9.5 (interquartile range (IQR): 5.5–15.7) versus MO: 17.0 (IQR: 10.9–34.2) versus MA: 20.9 (IQR: 11.7–29.3), overall p value <0.0001, but not for adiponectin levels. After full adjustment, the third of leptin was positively associated with MA (OR 2.89 (1.00–8.4)) and the second of adiponectin was associated with MO (OR 2.76; 95% CI: 1.09–6.96, p = 0.03). Positive associations with MA, second (OR 3.81; 95% CI: 1.07–13.59; p = 0.04) and third tertile of leptin (6.54; 95% CI: 1.74–24.57, p = 0.005), were also observed in women, but not in men. Conclusions: Positive associations between ADP and migraine, particularly between MA and leptin levels in women, raise the possibility of adipocytokines and play a role in migraine pathophysiology.
Receptor-Like Kinase (RLK) as a candidate gene conferring resistance to Hemileia vastatrix in coffee

: The biotrophic fungus Hemileia vastatrix causes coffee leaf rust (CLR), one of the most devastating diseases in Coffea arabica . Coffee, like other plants, has developed effective mechanisms to recognize and respond to infections caused by pathogens. Plant resistance gene analogs (RGAs) have been identified in certain plants as candidates for resistance ( R ) genes or membrane receptors that activate the R genes. The RGAs identified in different plants possess conserved domains that play specific roles in the fight against pathogens. Despite the importance of RGAs, in coffee plants these genes and other molecular mechanisms of disease resistance are still unknown. This study aimed to sequence and characterize candidate genes from coffee plants with the potential for involvement in resistance to H. vastatrix . Sequencing was performed based on a library of bacterial artificial chromosomes (BAC) of the coffee clone ‘Híbrido de Timor’ (HdT) CIFC 832/2 and screened using a functional marker. Two RGAs, HdT_ LRR_RLK1 and HdT_LRR_RLK2, containing the motif of leucine-rich repeat-like kinase (LRR-RLK) were identified. Based on the presence or absence of the HdT_LRR_RLK2 RGA in a number of differential coffee clones containing different combinations of the rust resistance gene, these RGAs did not correspond to any resistance gene already characterized (S H 1-9). These genes were also analyzed using qPCR and demonstrated a major expression peak at 24 h after inoculation in both the compatible and incompatible interactions between coffee and H. vastatrix . These results are valuable information for breeding programs aimed at developing CLR-resistant cultivars, in addition to enabling a better understanding of the interactions between coffee and H. vastatrix .
Study of the Clustering Algorithms for Hyper Spectral Remote Sensing Images

The data taken from the hyperspectral images are discrete and hard to classify because they are arranged in the contiguous spectral bands. We can easily detect and classify the data from the spectral images if the number of attributes in the images is very little. But it is very difficult to segregate the data from the images if the numbers of classes are more. To make the segregation easy we implement the procedure that utilizes a clustering algorithm. This paper comprises of two sections, firstly to perform unsupervised learning using different types of clustering algorithms and secondly, to compare the efficiency of the resultant clustering of these different methods to prove that which clustering method is best suitable in reading the hyperspectral imaging data. For this I have used these clustering algorithms, they are DBSCAN, MiniBatch K-Means, K-Means. By comparing these techniques I surmised that the K-Means is better for using the HyperSpectral Imaging data. To perform these calculations I used the Matlab data set from the Computational Intelligence Group.
A Comparative Analysis Of Face Recognition Models On Masked Faces

: Face recognition systems are among the widely used biometrics in fields such as surveillance, access controls, attendance, forensics and other security purposes. Due to current Covid-19 crisis almost everyone can be seen wearing a mask in public. This change can be very challenging for existing facial recognition systems and can make them less effective. A face mask covers significant portion of the face making facial recognition systems having less face features to recognize and on top of that face masks can also add significant noise to the image. As wearing face masks is going to be new normal it is important to understand and study how current state of art face recognition models perform in recognizing masked faces. We did a comparative analysis on four state of art deep learning models which are widely used in this field 1)VGGFace 2)FaceNet 3)OpenFace 4)DeepFace. The analysis is made on face verification task on RMFRD dataset which is largest real world masked face dataset available. We compared the models on various metrics like error rate, accuracy ,precision, verification time. ——————————  ——————————
Autonomous Observation of Seasonal Carbonate Chemistry Dynamics in the Mid‐Atlantic Bight

Ocean acidification alters the oceanic carbonate system, increasing potential for ecological, economic, and cultural losses. Historically, productive coastal oceans lack vertically resolved high‐resolution carbonate system measurements on time scales relevant to organism ecology and life history. The recent development of a deep ion‐sensitive field‐effect transistor (ISFET)‐based pH sensor system integrated into a Slocum glider has provided a platform for achieving high‐resolution carbonate system profiles. From May 2018 to November 2019, seasonal deployments of the pH glider were conducted in the central Mid‐Atlantic Bight. Simultaneous measurements from the glider's pH and salinity sensors enabled the derivation of total alkalinity and calculation of other carbonate system parameters including aragonite saturation state. Carbonate system parameters were then mapped against other variables, such as temperature, dissolved oxygen, and chlorophyll, over space and time. The seasonal dynamics of carbonate chemistry presented here provide a baseline to begin identifying drivers of acidification in this vital economic zone. Plain Language Summary Seawater chemistry affects the ability of organisms to survive in the ocean. Past monitoring of seawater chemistry has missed key times and locations that are important to natural life cycles. In order to fill in those gaps, we put a chemical sensor into a deep‐sea robot that we can control from land. This robot, called a Slocum glider, glides from the top of the ocean down to 200‐m depth and collects ocean chemistry data along the way. We used our Slocum glider to measure how seawater chemistry differs between seasons in the Mid‐Atlantic, which will help us understand how organisms might be affected by water conditions.
High Morphological Differentiation in Crown Architecture Contrasts with Low Population Genetic Structure of German Norway Spruce Stands

High elevation sites in the low mountain ranges in Germany are naturally covered by Norway spruce (Picea abies (Karst.) L. ) stands. Historically, large scale anthropogenic range expansion starting in the mid to late 18th century had a huge impact on the forest composition throughout Germany. Utilisation and exploitation often led to artificial regeneration, mostly carried out using seeds from allochthonous provenances. Usually, autochthonous (natural) high elevation Norway spruce trees have narrow crown phenotypes, whereas lowland trees have broader crowns. Narrow crown phenotypes are likely the result of adaptation to heavy snow loads combined with high wind speeds. In the present study, neighbouring stand pairs of putative autochthonous and allochthonous origin with contrasting phenotypes in high elevation sites were investigated with 200 samples each. These stands are located in the Ore Mountains, the Thuringian Forest, and the Harz Mountains. Additionally, a relict population with the typical narrow high elevation phenotypes was sampled in Thuringia, known as “Schlossbergfichte”. The objective of the study was to quantify supposedly adaptive phenotypic differences in crown architecture and the genetic differentiation of 11 putatively neutral nuclear microsatellite markers (i.e., simple sequence repeats (nSSRs)). The high differentiation of morphological traits (PST = 0.952–0.989) between the neighbouring autochthonous and allochthonous stands of similar age contrasts with the very low neutral genetic differentiation (FST = 0.002–0.007; G″ST = 0.002–0.030), suggesting that directional selection at adaptive gene loci was involved in phenotypic differentiation. Comparing the regions, a small isolation by distance effect for the Harz Mountains was detected, suggesting landscape resistance restricting gene flow. Finally, the differentiation of the very old autochthonous (up to 250 years) stand “Schlossbergfichte” with typical high elevation phenotypes could cohere with the sampling of a relict genepool.