Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Anxiety based on individual level engagements on YouTube & Google Search Engine

Anxiety disorder is one of the most prevalent mental health conditions, arising from complex interactions of biological and environmental factors and severely interfering one's ability to lead normal life activities. Current methods for detecting anxiety heavily rely on in-person interviews. Yet, such mental health assessments and surveys can be expensive, time consuming, and blocked by social stigmas. In this work, we propose an alternative way to identify individuals with anxiety and further estimate their levels of anxiety using private online activities histories from YouTube and Google search engine, platforms that are used by millions of people daily. We ran a longitudinal study and collected multiple rounds of anonymized YouTube and Google search log data from volunteering participants, along with their clinically validated ground-truth anxiety assessment scores. We then engineered explainable features that capture both the temporal and semantic aspects of online behaviors. We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores with a mean square error of 1.87 based on the ubiquitous individual-level online engagements.
THE ONTOGENETIC NICHE AND SPECIES INTERACTIONS IN SIZE-STRUCTURED POPULATIONS

Body size is manifestly one of the most important attributes of an organism from an ecological and evolutionary point of view. Size has a predominant influence on an animal's energetic requirements, its potential for resource exploitation, and its susceptibility to natural enemies. A large literature now exists on how physiological, life history, and population parameters scale with body dimensions (24, 131). The ecological literature on species interactions and the structure of animal communities also stresses the importance of body size. Differences in body size are a major means by which species avoid direct overlap in resource use (153), and size-selective predation can be a primary organizing force in some communities (20, 70). Size thus imposes important constraints on the manner in which an organism interacts with its environment and influences the strength, type, and symmetry of interactions with other species (152, 207). Paradoxically, ecologists have virtually ignored the implications of these observations for interactions among species that exhibit size-distributed populations. For instance, it has been often suggested that competing species
How Plants Obtain Predatory Mites as Bodyguards

Phytophagous mites are a serious threat to their host plants; in absence of predators they tend to overexploit their food source. To prevent such a crash and maintain as much leaf area as possible host plants may defend themselves in various ways, one of which is to increase the effectiveness of natural enemies of the phytophagous mites. Predatory mites are considered to be very important natural enemies of plant-feeding mites and there is evidence for a mutualistic interaction with plants. Examples of how plants obtain and arrest predatory mites as bodyguards are discussed. It is known for a long time that some plant species provide pollen that appear to be a very profitable food source for some species of predatory mites: it does not only promote survival, but also allows development and egg production. In doing so, plants ensure themselves of bodyguards even before any damage is inflicted. Recently, evidence has been obtained that plants under attack by spider mites provide information by releasing a blend of volatile chemicals that are helpful to predatory mites in locating their prey. Plant-predator interactions are not always of a mutualistic nature. Some plant species invest in a rigorous defence against spider mites, even though this may be to the detriment of the predators: glandular hairs of some plant species entrap not only spider mites, but also their predators. The evolutionary implications of these various plant-predator interactions are discussed.
Equivalence of Approaches to Relational Quantum Dynamics in Relativistic Settings

We have previously shown that three approaches to relational quantum dynamics—relational Dirac observables, the Page-Wootters formalism and quantum deparametrizations—are equivalent. Here we show that this “trinity” of relational quantum dynamics holds in relativistic settings per frequency superselection sector. Time according to a clock subsystem is defined via a positive operator-valued measure (POVM) that is covariant with respect to the group generated by its (quadratic) Hamiltonian. This differs from the usual choice of a self-adjoint clock observable conjugate to the clock momentum. It also resolves Kuchař's criticism that the Page-Wootters formalism yields incorrect localization probabilities for the relativistic particle when conditioning on a Minkowski time operator. We show that conditioning instead on the covariant clock POVM results in a Newton-Wigner type localization probability commonly used in relativistic quantum mechanics. By establishing the equivalence mentioned above, we also assign a consistent conditional-probability interpretation to relational observables and deparametrizations. Finally, we expand a recent method of changing temporal reference frames, and show how to transform states and observables frequency-sector-wise. We use this method to discuss an indirect clock self-reference effect and explore the state and temporal frame-dependence of the task of comparing and synchronizing different quantum clocks.
Effects of Breastfeeding and Formula Feeding on the Expression Level of FTO, CPT1A and PPAR-α Genes in Healthy Infants

Purpose The study aimed to investigate the effect of breastfeeding, formula feeding and mix feeding (breastfed plus formula-fed) on the expression level of obesity-predisposing genes including fat mass and obesity-associated (FTO), carnitine palmitoyltransferase 1A (CPT1A), and peroxisome proliferator-activated receptor-α (PPAR-α) in 5- to 6-month-old infants. Patients and Methods A total of 150 infants participated in this case–control study. All subjects were healthy infants aged 5–6 months that divided into 3 groups: breastfed, formula-fed, and mix-fed. The expression level of FTO, CPT1A, and PPAR-α genes in peripheral blood mononuclear cells (PBMC) was evaluated in each group using reverse transcription-polymerase chain reaction (RT-PCR) method. Results Our findings showed that the current weight, height, and head circumference of infants in the formula feeding and mix feeding groups were significantly higher than those in the exclusive breastfeeding group. The expression level of FTO and CPT1A genes in formula-fed and mix-fed infants was significantly higher (p<0.001) than that in breastfed infants, while the expression level of PPAR-α gene was significantly lower (p<0.05). Conclusion Breastfeeding showed modulatory effects on the expression level of obesity-predisposing genes and can protect against obesity and subsequent non-communicable diseases. However, more investigations are required to explain the epigenetic effects of breast milk.
Metodologia para ensaios de candidatos vacinais contra leishmaniose visceral canina pelo co-cultivo de linfócitos e macrófagos infectados com Leishmania chagasi.

In the last 30 years, visceral leishmaniasis (VL) has moved to urban and peri-urban areas, highlighting the dog as the main reservoir of the L. chagasi (syn. L. infantum). In this context, researchers to consider as a consensus that a vaccine against canine visceral leishmaniasis (CVL) would be an important tool in the control of both human and canine visceral leishmaniasis. For this reason, we developed a new method for accessing the immune system of dogs that would be employed as the alternative for analyzing of immunogenicity and protection rate induced by vaccine candidates against CVL. For this purpose, we employed peripheral blood mononuclear cells (PBMC) of healthy dogs for standardizing the: (i) in vitro conditions of canine monocyte-derived macrophages (CM-DM) cultivation previously infected by L. chagasi and evaluated 3, 24, 48, 72 and 96 hours post-infection at the times two, three, four and five days of CM-DM differentiation (n=3 healthy dogs); (ii) the purification of T-cells subsets (CD4+ and CD8+); (iii) L. chagasi infected CM-DM co-cultured with purified T-cells subsets (CD4+ and/or CD8+) evaluated at a lymphocytes:macrophage ratio ranging from 1:5 to 2:1 (n=12 healthy dogs). In all the experiments we analyzed in the culture supernatant different immunological biomarkers, such as: nitric oxide levels, myeloperoxidase (MPO) and N-acetylglucosaminidase (NAG) production; type 1 (IFN-, TNF-, IL-12), type 2 (IL-4) and immunomodulatory (IL-10) cytokines; and described the frequency of parasitism and parasite load in L. chagasi-infected CM-DM. Our major data revealed that after five days and at 72h post L. chagasi-infection CM-DM presented a better pattern of both morphology of the culture and microbicidal ability. Besides the levels of NAG remained practically unchanged, MPO displayed a significant reduction at five days of CM-DM differentiation, indicating lower counts of granulocytes at this period. The CM-DM co-culture experiments using purified T-cells subsets showed an outstanding profile in the immunological biomarkers and microbicidal activity considering the lymphocytes (CD4+ or CD8+) : macrophages ratio of 1:2 and 1:1:1 for lymphocytes (CD4+ and CD8+):macrophages. In this sense, these conditions in the rate of co-culture revealed an association between nitric oxide production and the levels of type 1, type 2 and immunomodulatory cytokines with the maintenance of intermediated frequency in the parasitism and the stabilization of parasite burden in the L. chagasi-infected CM-DM. Our data stimulates to further analyze this methodology in CM-DM co-cultured with purified CD4+ and/or CD8+ T-cells from PBMC of dogs immunized with vaccine candidates against CVL.
Predicting In-Game Actions from Interviews of NBA Players

Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. Although there is an abundance of computational work on player metrics prediction based on past performance, very few attempts to incorporate out-of-game signals have been made. Specifically, it was previously unclear whether linguistic signals gathered from players’ interviews can add information that does not appear in performance metrics. To bridge that gap, we define text classification tasks of predicting deviations from mean in NBA players’ in-game actions, which are associated with strategic choices, player behavior, and risk, using their choice of language prior to the game. We collected a data set of transcripts from key NBA players’ pre-game interviews and their in-game performance metrics, totalling 5,226 interview-metric pairs. We design neural models for players’ action prediction based on increasingly more complex aspects of the language signals in their open-ended interviews. Our models can make their predictions based on the textual signal alone, or on a combination of that signal with signals from past-performance metrics. Our text-based models outperform strong baselines trained on performance metrics only, demonstrating the importance of language usage for action prediction. Moreover, the models that utilize both textual input and past-performance metrics produced the best results. Finally, as neural networks are notoriously difficult to interpret, we propose a method for gaining further insight into what our models have learned. Particularly, we present a latent Dirichlet allocation–based analysis, where we interpret model predictions in terms of correlated topics. We find that our best performing textual model is most associated with topics that are intuitively related to each prediction task and that better models yield higher correlation with more informative topics.1
Food chain effects of nutrient limitation in primary producers

The propagation of mineral limitation in primary producers to the second consumer level has rarely been investigated. Recently, it has been shown that limitation effects do travel up the food chain, not only quantitatively, but also qualitatively, and also that these quality effects affect the nutritional condition and growth of secondary consumers. The present study experimentally investigated the effect of phosphorus limitation in combination with fatty acid addition in primary producers (Scenedesmus obliquus) channelled through a primary consumer (Daphnia magna) on the condition of larval rainbow trout (Oncorhynchus mykiss). The C : P ratio and the fatty acid concentrations of the primary producers varied significantly with the phosphorus concentration of the culture media. These differences were also visible in the primary consumers (D. magna) feeding on the algae. The significantly different stoichiometry and fatty acid concentrations of the daphnids fed to larval trout did not lead to significant differences in growth, but the additional supplementation of fatty acid emulsions caused a significant increase in the condition of the fish. It was found that in the case of Daphnia as the primary prey for fish, with its relatively high phosphorus content even under phosphorus limitation, it is unlikely that phosphorus limitation affects fish condition and growth.
Distributed Linearly Separable Computation

This paper formulates a distributed computation problem, where a master asks <inline-formula> <tex-math notation="LaTeX">${\mathsf N}$ </tex-math></inline-formula> distributed workers to compute a linearly separable function. The task function can be expressed as <inline-formula> <tex-math notation="LaTeX">${\mathsf K}_{\mathrm{ c}}$ </tex-math></inline-formula> linear combinations of <inline-formula> <tex-math notation="LaTeX">${\mathsf K}$ </tex-math></inline-formula> messages, where each message is a function of one dataset. Our objective is to find the optimal tradeoff between the computation cost (number of uncoded datasets assigned to each worker) and the communication cost (number of symbols the master must download), such that from the answers of any <inline-formula> <tex-math notation="LaTeX">${\mathsf N}_{\mathrm{ r}}$ </tex-math></inline-formula> out of <inline-formula> <tex-math notation="LaTeX">${\mathsf N}$ </tex-math></inline-formula> workers the master can recover the task function with high probability, where the coefficients of the <inline-formula> <tex-math notation="LaTeX">${\mathsf K}_{\mathrm{ c}}$ </tex-math></inline-formula> linear combinations are uniformly i.i. d. over some large enough finite field. The formulated problem can be seen as a generalized version of some existing problems, such as distributed gradient coding and distributed linear transform. In this paper, we consider the specific case where the computation cost is minimum, and propose novel achievability schemes and converse bounds for the optimal communication cost. Achievability and converse bounds coincide for some system parameters; when they do not match, we prove that the achievable distributed computing scheme is optimal under the constraint of a widely used ‘cyclic assignment’ scheme on the datasets. Our results also show that when <inline-formula> <tex-math notation="LaTeX">${\mathsf K}= {\mathsf N}$ </tex-math></inline-formula>, with the same communication cost as the optimal distributed gradient coding scheme proposed by Tandon <italic>et al</italic>. from which the master recovers one linear combination of <inline-formula> <tex-math notation="LaTeX">${\mathsf K}$ </tex-math></inline-formula> messages, our proposed scheme can let the master recover any additional <inline-formula> <tex-math notation="LaTeX">${\mathsf N}_{\mathrm{ r}}-1$ </tex-math></inline-formula> independent linear combinations of messages with high probability.
HYDRA: Pruning Adversarially Robust Neural Networks

In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural network size (often millions of parameters). While the research community has extensively explored the use of robust training and network pruning independently to address one of these challenges, only a few recent works have studied them jointly. However, these works inherit a heuristic pruning strategy that was developed for benign training, which performs poorly when integrated with robust training techniques, including adversarial training and verifiable robust training. To overcome this challenge, we propose to make pruning techniques aware of the robust training objective and let the training objective guide the search for which connections to prune. We realize this insight by formulating the pruning objective as an empirical risk minimization problem which is solved efficiently using SGD. We demonstrate that our approach, titled HYDRA, achieves compressed networks with state-of-the-art benign and robust accuracy, simultaneously. We demonstrate the success of our approach across CIFAR-10, SVHN, and ImageNet dataset with four robust training techniques: iterative adversarial training, randomized smoothing, MixTrain, and CROWN-IBP. We also demonstrate the existence of highly robust sub-networks within non-robust networks. Our code and compressed networks are publicly available at \url{this https URL}.
Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation

Purpose To identify key pathogenic genes and reveal the potential molecular mechanisms of endometrial cancer (EC) using bioinformatics analysis and immunohistochemistry validation. Materials and Methods Through weighted gene co-expression network analysis (WGCNA), a co-expression network was constructed based on the top 25% variant genes in the GSE50830 dataset downloaded from gene expression omnibus (GEO). GO and KEGG pathway enrichment analyses were performed using the DAVID online tool. Candidate genes were selected using the cytoHubba plug-in of Cytoscape, mRNA expression levels and prognostic values in EC were analyzed by Oncomine, GEPIA, and Kaplan–Meier Plotter database to determine hub genes. One hub gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endometrial tissues and TCGA-UCEC cohort. Genes co-expressed with this hub gene were identified by LinkedOmics. Finally, its correlation with immune infiltration was evaluated by TIMER. Results Three co-expression modules and five candidate genes in each module were obtained by WGCNA; four hub genes were identified (LGR5, SST, ZNF558, and PTGDS). The mRNA levels of LGR5 and SST were significantly upregulated in EC, whereas those of ZNF558 and PTGDS were significantly downregulated; the expression of all four genes was associated with EC prognosis. Further validation demonstrated that PTGDS was significantly downregulated in the EC group compared with the atypical hyperplasia and normal endometrial groups, and its low expression was an independent risk factor for worse prognosis of EC. Biological function analysis indicated that PTGDS might be involved in the adaptive immune response, leukocyte migration, as well as in the regulation of cell adhesion molecules and chemokine signaling. Additionally, PTGDS expression was positively correlated with immune infiltration status of B cells, CD4+ T cells and macrophages. Conclusion LGR5, SST, ZNF558, and PTGDS may participate in the development, progression, and prognosis of EC, in which PTGDS may be a novel biomarker and therapeutic target for EC.
The effect of prey abundance and foraging tactics on the population dynamics of a social, territorial carnivore, the spotted hyena

We used naturally occurring spatial and temporal changes in prey abundance to investigate whether the foraging behavior of a social, territorial carnivore, the spotted hyena (Crocuta crocuta), conformed to predictions derived from the ideal free distribution. We demonstrate that hyenas in the Ngorongoro Crater, Tanzania, redistributed themselves from less profitable to more profitable areas, even when this required them to undertake foraging trips to areas beyond their clan territory boundary, or required normally philopatric females to emigrate. As expected for a system with rank related access to food resources in the territory, females of low social status foraged more often outside their territory and were more likely to emigrate than dominant females. Probably because Crater hyenas regularly foraged outside their territories, there was no correlation between clan size and prey density within territories, suggesting that clan sizes may have exceeded the carrying capacity of territories. A substantial decline of the hyena population in the Crater from 385 adults in the mid 1960s to 117 in 1996 was most likely due to a substantial decline of their main prey. The decline in the hyena population was associated with a decline in the size of clans but not in the number of clans. The number of clans probably remained constant due to emigration by females from large clans into vacant areas or clans with no adult females, and because hyenas regularly fed in areas containing concentrations of prey beyond their territory boundary. Between 1996 and 2003 annual recruitment rates of Crater hyenas consistently exceeded annual mortality rates, resulting in an almost doubling of the adult population. This increase was most likely due to an increase in prey abundance, a relatively low level of predation on hyenas by lions (Panthera leo), and an absence of high levels of disease related mortality.
Influence of climate change on production of secondary chemicals in high altitude medicinal plants: Issues needs immediate attention

Medicinal plants are highly valuable to human livelihood and the Indian Himalaya is well recognized amongst unique and globally important medicinal plant rich areas. Studies on possible effects of climate change on medicinal plants are particularly significant due to their value within traditional systems of medicine and as economically useful plants. There are evidences that climate change is causing noticeable effects on life cycles and distribution of the plant species. However, it is largely unclear about climate change effects on secondary chemicals production in plants and particularly in high altitude medicinal plants of Indian Himalaya. Elsewhere this perspective has been given renewed attention recently. A need for research to improve our understanding of these effects on high-altitude medicinal plants is stressed in the present article.