Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel anticancer property of Lycium barbarum polysaccharide in triggering ferroptosis of breast cancer cells

Breast cancer is one of the most malignant tumors and is associated with high mortality rates among women. Lycium barbarum polysaccharide (LBP) is an extract from the fruits of the traditional Chinese herb, L. barbarum. LBP is a promising anticancer drug, due to its high activity and low toxicity. Although it has anticancer properties, its mechanisms of action have not been fully established. Ferroptosis, which is a novel anticancer strategy, is a cell death mechanism that relies on iron-dependent lipid reactive oxygen species (ROS) accumulation. In this study, human breast cancer cells (Michigan Cancer Foundation-7 (MCF-7) and MD Anderson-Metastatic Breast-231 (MDA-MB-231)) were treated with LBP. LBP inhibited their viability and proliferation in association with high levels of ferroptosis. Therefore, we aimed to ascertain whether LBP reduced cell viability through ferroptosis. We found that the structure and function of mitochondria, lipid peroxidation, and expression of solute carrier family 7 member 11 (SLC7A11, also known as xCT, the light-chain subunit of cystine/glutamate antiporter system Xc−) and glutathione peroxidase 4 (GPX4) were altered by LBP. Moreover, the ferroptosis inhibitor, Ferrostatin-1 (Fer-1), rescued LBP-induced ferroptosis-associated events including reduced cell viability and glutathione (GSH) production, accumulation of intracellular free divalent iron ions and malondialdehyde (MDA), and down-regulation of the expression of xCT and GPX4. Erastin (xCT inhibitor) and RSL3 (GPX4 inhibitor) inhibited the expression of xCT and GPX4, respectively, which was lower after the co-treatment of LBP with Erastin and RSL3. These results suggest that LBP effectively prevents breast cancer cell proliferation and promotes ferroptosis via the xCT/GPX4 pathway. Therefore, LBP exhibits novel anticancer properties by triggering ferroptosis, and may be a potential therapeutic option for breast cancer.
Towards to optimisation of road traffic noise protection design

U radu je ukazano, sukladno regulativi, na nužnost izrade projekta zastite od buke za svaku cestu, a detaljnost projektne dokumentacije određena je razinom projekta (idejni, glavni, izvedbeni). Projektiranje zastite od buke zahtjeva izradu modela prostora koji ima određene specificnosti na koje se u radu ukazuje. Isto tako ukazano je na mogucnosti primjene njemacke RLS 90 metode proracuna razine buke cestovnog prometa u urbanim sredinama. Na primjerima su pokazane razlike u dobivenim rezultatima ako se primjenjuje njemacka RLS 90 metoda (siroka primjena u projektantskoj praksi) u odnosu na preporucenu francusku NMPB metodu koja je preporucena za primjenu europskom direktivom, a u u novije je vrijeme hrvatskom regulativom propisana za proracun razina buke od cestovnog prometa pri izradi strateskih karata buke.
Neurocognitive and neural mechanisms underlying deficit on the Reading Mind In The Eyes Task: Evidence from patients with focal prefrontal cortex damage

ABSTRACT Objective Conflicting evidence has arisen from the few correlational studies that have examined the relationship between the ability to decode social emotional cues through the eyes and executive functions in individuals with prefrontal cortex damage. The objective of the current study was (1) to investigate the impact of both focal prefrontal and parietal cortex damage on both of these domains; (2) to examine whether impaired ability to decode social emotion cues through the eyes was predicted by executive function deficits and; (3) to explore the neural correlates of both of these components. Method Thirty individuals with prefrontal cortex damage, 15 individuals with parietal cortex damage and 30 matched healthy comparison subjects were subjected to a battery of executive tasks assessing inhibition, flexibility, and planning processes and the Reading the Mind in the Eyes Task (RMET) assessing ability to decode social emotion cues through the eyes. Results Compared to both comparison groups, individuals with prefrontal cortex damage were impaired in performing RMET and almost all executive tasks. Regression analysis showed that for individuals with prefrontal cortex damage, performance on the Stroop and Hayling tasks predicted scores on the RMET, while for the healthy comparison group, only the verbal fluency performance predicted scores on the RMET. The Voxel lesions-symptom mapping technique showed that both components rely partly on the same neural substrates, reflecting beyond the neural network of RMET per se that of the cognitive processes elicited by this multi-determinant task. Conclusion These findings suggest that the ability to read mental states of mind depends on the inhibition and flexibility executive mechanisms.
Shared genetic background between SARS-CoV-2 infection and large artery stroke

Background and aims: Increased risk of stroke, particularly large artery stroke (LAS), has been observed in patients with COVID-19. The biological processes underlying the observed higher risk are still unknown. We explored the association between stroke subtypes and COVID-19 susceptibility to understand whether biological mechanisms specific to SARS-CoV-2 uptake/infection could be leading to excess stroke risk in this population. Patients and methods: We constructed a polygenic risk score (PRS) of COVID-19 susceptibility and tested its association with stroke subtypes using individual- and summary-level genetic data (SiGN, MEGASTROKE). We generated co-expression networks of genes involved in SARS-CoV-2 uptake/infection (ACE2, TMPRSS2, BEST3, ISLR2 and ADAM17) based on existing tissue expression libraries. Gene-based association testing was performed using S-PrediXcan and VEGAS2. Permutation independence tests were performed to assess SARS-CoV-2-related gene enrichment in stroke and its subtypes. Results: Our PRS demonstrated an association between COVID-19 susceptibility and LAS in SiGN (OR = 1.05 per SD increase, 95% CI: (1.00, 1.10), p = 0.04) and MEGASTROKE (β = 0.510, 95% CI: (0.242, 0.779), FDR-p = 0.0019). The SARS-CoV-2 risk-related ISLR2 co-expression gene network was significantly associated with genetic risk of LAS in aorta, tibial arteries, and multiple brain regions (P < 0.05). Conclusion: Presence of genetic correlation and significant pathway enrichment suggest that increases in LAS risk reported in COVID-19 patients may be intrinsic to the viral infection, rather than a more generalized response to severe illness.
TIMES OF EFFICIENCY AND QUALITY OF SOYBEAN CROP MECHANICAL OPERATION IN GEOMETRY FUNCTIONS OF PLOTS

The knowledge of the operational quality of soybean harvester provides useful information to management in order to obtain the maximum performance of all available resources, with minimal expenses. The aimed of this study was to evaluate the quality of mechanized soybean harvesting operation in different formats of plots through statistical process control. Treatments were established from the formats of existing plots in the area (irregular, trapezoidal and rectangular). The activities carried out during the harvest were monitored (harvesting, unloading, handling, maneuvering and climate charts) and through these activities were made the calculation of capacity and harvesting operation efficiencies. In the determination of total losses were used 4 circular frames of 0.33 m2 each, the grain losses were considered below and above these frames. The statistical analysis was by means of statistical process control opting for the use of the type CUSUM charts. The rectangular plot showed greater management efficiency and harvesting efficiency. The trapezoidal shape facilitated the maneuvers relative to others. The rectangular plot has better quality of operation in relation to others. The CUSUM control chart showed to be effective in preventing instability and maintenance of process quality.
Stool is a sensitive and noninvasive source of DNA for monitoring expansion in repeat expansion disease mouse models

ABSTRACT Repeat expansion diseases are a large group of human genetic disorders caused by expansion of a specific short tandem repeat tract. Expansion in somatic cells affects age of onset and disease severity in some of these disorders. However, alleles in DNA derived from blood, a commonly used source of DNA, usually show much less expansion than disease-relevant cells in the central nervous system in both humans and mouse models. Here we examined the extent of expansion in different DNA sources from mouse models of the fragile X-related disorders, Huntington's disease, spinocerebellar ataxia type 1 and spinocerebellar ataxia type 2. We found that DNA isolated from stool is a much better indicator of somatic expansion than DNA from blood. As stool is a sensitive and noninvasive source of DNA, it can be useful for studies of factors affecting the risk of expansion, or the monitoring of treatments aimed at reducing expansion in preclinical trials, as it would allow expansions to be examined longitudinally in the same animal and allow significant changes in expansion to be observed much earlier than is possible with other DNA sources.
The marketing of food to children : moving the debate beyond television advertising.

Whilst food marketing to children has been heavily discussed in both the political and marketing arenas over the past few years, it has predominately concentrated on television advertising. However, it is now recognised that contemporary marketers are moving away from television advertising and expanding their range of communications. Therefore, this thesis looks to widen the debate to other forms of food marketing communications. From previous literature and four background interviews a conceptual framework is developed which highlights the importance of four parties: the food companies (and their communication agencies), the regulatory bodies, parents and children. The first two parties are predominantly investigated using secondary data, whilst the primary research for this study concentrates on the second two parties. As such fourteen qualitative family interviews with children aged between seven and eleven are undertaken. From my findings, the original conceptual framework is developed into an expanded framework. The framework has four sections. The first section details seventeen types of communications currently being used by the UK food industry. The second shows four types of restrictions (two frequency restrictions and two impact restrictions) which parents use to mediate some of these communications. The third section confirms that children possess differing levels of understanding across the range of communications. Finally the fourth section highlights (1) that there are five communications (television advertising, free gifts, price promotions, tie-ins with television, film and cartoon characters and children's speciality foods) which children perceive they encounter frequently and which have an impact on their purchase requests and (2) that there are seven communications (print advertising, product placement in television and films, product placement in video games, online advertising, advergames, in-school communications and branded toys) which they perceive they infrequently encounter and which have little impact. Overall this thesis is, to my knowledge, the first to present a holistic consideration of the marketing of food to children in the UK. It not only includes a full range of communications but also all the complete process, from the food companies to the children.
Zero-shot Cross-lingual Conversational Semantic Role Labeling

While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of translation-based methods, we present a simple but effective approach to perform zero-shot cross-lingual CSRL. Our model implicitly learns language-agnostic, conversational structure-aware and semantically rich representations with the hierarchical encoders and elaborately designed pre-training objectives. Experimental results show that our model outperforms all baselines by large margins on two newly collected English CSRL test sets. More importantly, we confirm the usefulness of CSRL to non-Chinese conversational tasks such as the question-in-context rewriting task in English and the multi-turn dialogue response generation tasks in English, German and Japanese by incorporating the CSRL information into the downstream conversation-based models. We believe this finding is significant and will facilitate the research of non-Chinese dialogue tasks which suffer the problems of ellipsis and anaphora.
On the slice spectral sequence for quotients of norms of Real bordism

In this paper, we investigate equivariant quotients of the Real bordism spectrum's multiplicative norm $MU^{((C_{2^n}))}$ by permutation summands. These quotients are of interest because of their close relationship with higher real $K$-theories. We introduce new techniques for computing the equivariant homotopy groups of such quotients. As a new example, we examine the theories $BP^{((C_{2^n}))}\langle m,m\rangle$. These spectra serve as natural equivariant generalizations of connective integral Morava $K$-theories. We provide a complete computation of the $a_{\sigma}$-localized slice spectral sequence of $i^*_{C_{2^{n-1}}}BP^{((C_{2^n}))}\langle m,m\rangle$, where $\sigma$ is the real sign representation of $C_{2^{n-1}}$. To achieve this computation, we establish a correspondence between this localized slice spectral sequence and the $H\mathbb{F}_2$-based Adams spectral sequence in the category of $H\mathbb{F}_2 \wedge H\mathbb{F}_2$-modules. Furthermore, we provide a full computation of the $a_{\lambda}$-localized slice spectral sequence of the height-4 theory $BP^{((C_{4}))}\langle 2,2\rangle$. The $C_4$-slice spectral sequence can be entirely recovered from this computation.
Deep Conditional Representation Learning for Drum Sample Retrieval by Vocalisation

Imitating musical instruments with the human voice is an efﬁcient way of communicating ideas between music producers, from sketching melody lines to clarifying desired sonorities. For this reason, there is an increasing interest in building applications that allow artists to efﬁciently pick target samples from big sound libraries just by imitating them vocally. In this study, we investigated the potential of conditional autoencoder models to learn informative features for Drum Sample Retrieval by Vocalisation (DSRV). We assessed the usefulness of their embeddings using four evaluation metrics, two of them relative to their acoustic properties and two of them relative to their perceptual properties via human listeners’ similarity ratings. Results suggest that models conditioned on both sound-type labels (drum vs imitation) and drum-type labels (kick vs snare vs closed hi-hat vs opened hi-hat) learn the most informative embeddings for DSRV. We ﬁnally looked into individual differences in vocal imitation style via the Mantel test and found salient differences among participants, highlighting the importance of user information when designing DSRV systems. ex-celled in both acoustics- and perception-based metrics. Fu-ture work will explore ways to ﬁne-tune predictions by building user-based DSRV systems based on the CAE-SDL embeddings.
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning

Pre-trained sequence-to-sequence language models have led to widespread success in many natural language generation tasks. However, there has been relatively less work on analyzing their ability to generate structured outputs such as graphs. Unlike natural language, graphs have distinct structural and semantic properties in the context of a downstream NLP task, e.g., generating a graph that is connected and acyclic can be attributed to its structural constraints, while the semantics of a graph can refer to how meaningfully an edge represents the relation between two node concepts. In this work, we study pre-trained language models that generate explanation graphs in an end-to-end manner and analyze their ability to learn the structural constraints and semantics of such graphs. We first show that with limited supervision, pre-trained language models often generate graphs that either violate these constraints or are semantically incoherent. Since curating large amount of human-annotated graphs is expensive and tedious, we propose simple yet effective ways of graph perturbations via node and edge edit operations that lead to structurally and semantically positive and negative graphs. Next, we leverage these graphs in different contrastive learning models with Max-Margin and InfoNCE losses. Our methods lead to significant improvements in both structural and semantic accuracy of explanation graphs and also generalize to other similar graph generation tasks. Lastly, we show that human errors are the best negatives for contrastive learning and also that automatically generating more such human-like negative graphs can lead to further improvements.
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning

Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names.Generative methods achieve remarkable performances in general domain EL with less memory usage while requiring expensive pre-training.Previous biomedical EL methods leverage synonyms from knowledge bases (KB) which is not trivial to inject into a generative method.In this work, we use a generative approach to model biomedical EL and propose to inject synonyms knowledge in it.We propose KB-guided pre-training by constructing synthetic samples with synonyms and definitions from KB and require the model to recover concept names.We also propose synonyms-aware fine-tuning to select concept names for training, and propose decoder prompt and multi-synonyms constrained prefix tree for inference.Our method achieves state-of-the-art results on several biomedical EL tasks without candidate selection which displays the effectiveness of proposed pre-training and fine-tuning strategies. The source code is available at https://github.com/Yuanhy1997/GenBioEL.
A Review of Datasets for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects. Numerous yet scattered corpora for ABSA make it difficult for researchers to identify corpora best suited for a specific ABSA subtask quickly. This study aims to present a database of corpora that can be used to train and assess autonomous ABSA systems. Additionally, we provide an overview of the major corpora for ABSA and its subtasks and highlight several features that researchers should consider when selecting a corpus. Finally, we discuss the advantages and disadvantages of current collection approaches and make recommendations for future corpora creation. This survey examines 65 publicly available ABSA datasets covering over 25 domains, including 45 English and 20 other languages datasets.
Is My Driver Observation Model Overconfident? Input-Guided Calibration Networks for Reliable and Interpretable Confidence Estimates

Driver observation models are rarely deployed under perfect conditions. In practice, illumination, camera placement and type differ from the ones present during training and unforeseen behaviours may occur at any time. While observing the human behind the steering wheel leads to more intuitive human-vehicle-interaction and safer driving, it requires recognition algorithms which do not only predict the correct driver state, but also determine their prediction quality through realistic and interpretable confidence measures. Reliable uncertainty estimates are crucial for building trust and are a serious obstacle for deploying activity recognition networks in real driving systems. In this work, we for the first time examine how well the confidence values of modern driver observation models indeed match the probability of the correct outcome and show that raw neural network-based approaches tend to significantly overestimate their prediction quality. To correct this misalignment between the confidence values and the actual uncertainty, we consider two strategies. First, we enhance two activity recognition models often used for driver observation with temperature scaling – an off-the-shelf method for confidence calibration in image classification. Then, we introduce Calibrated Action Recognition with Input Guidance (CARING) – a novel approach leveraging an additional neural network to learn scaling the confidences depending on the video representation. Extensive experiments on the Drive&Act dataset demonstrate that both strategies drastically improve the quality of model confidences, while our CARING model outperforms both, the original architectures and their temperature scaling enhancement, leading to best uncertainty estimates.
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective

Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., BYOL and Barlow Twins. Both types of approaches often involve learning neural feature extractors such that the embeddings of data exhibit high cross-view correlations. Although intuitive, the effectiveness of correlation-based neural embedding is mostly empirically validated. This work aims to understand latent correlation maximization-based deep multiview learning from a latent component identification viewpoint. An intuitive generative model of multiview data is adopted, where the views are different nonlinear mixtures of shared and private components. Since the shared components are view/distortion-invariant, representing the data using such components is believed to reveal the identity of the samples effectively and robustly. Under this model, latent correlation maximization is shown to guarantee the extraction of the shared components across views (up to certain ambiguities). In addition, it is further shown that the private information in each view can be provably disentangled from the shared using proper regularization design. A finite sample analysis, which has been rare in nonlinear mixture identifiability study, is also presented. The theoretical results and newly designed regularization are tested on a series of tasks.