Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of long-term administration of various dietary prebiotic supplements on the growth, immune cell activity and digestive tract histology of juvenile vimba (Vimba vimba)

Abstract Introduction The experiment was set to determine the effects of long-term (55-day) use of three commercial prebiotics including Saccharomyces cerevisiae–derived β-glucans and one including inulin on juvenile vimba (Vimba vimba) reared intensively under controlled conditions. Material and Methods Six-month-old fish were fed commercial feed (Control group, n = 90), or the same feed supplemented with 0.02% Leiber Beta-S (BS group, n = 90), 0.20% Biolex MB40 (MB group, n = 90), 0.30% CeFi (CE group, n = 90) or 1.00% inulin Orafti GR (IN group, n = 90) for 55 days. Results In the BS group, the final growth parameters were significantly lower than in the Control group, while the feed conversion ratio was significantly higher. No significant differences were found between any other group and the Control group in the respective parameters. The respiratory burst activity of the head-kidney phagocytes was significantly lower in all fish groups fed the prebiotic-supplemented diets compared to the Control group. The proliferative response of the head-kidney lymphocytes stimulated by concanavalin A was lower in the BS group than in the Control group, while in other groups this response was not affected. No significant differences were found in histopathological analyses of the digestive tract, liver or pancreas. Conclusion The long-term supplementation of fish diets with prebiotics can negatively influence the growth, feed conversion, nonspecific cellular resistance and proliferative activity of the T lymphocytes of vimba juveniles.
Adaptation at the edge: Patterns of local adaptation and genetic variation during a contemporary range expansion

During range expansion, differences can evolve between populations at the core and expanding edge of a range. While theory and experimental work has focused on range expansions across uniform environments, natural range expansions often occur over environmental gradients, which present novel selection pressures. We seek to understand how genetic variation expressed in different environments may constrain adaptation during range expansion across environmental gradients, by testing whether long-established populations are better adapted to their local environments than newly established populations in the expanding edge. We study the timing of winter dormancy in a beetle introduced for biological control (Diorhabda carinulata), expanding from areas with cold winters to areas with milder, shorter winters. In a reciprocal environment experiment, core populations showed a pattern of local adaptation, but only some edge populations showed a similar pattern, indicating these populations vary in their degree of adaptation. Expressed genetic variation of dormancy timing in a core population was high in a local (core) environment but disappeared in a novel (edge) environment. These results show that adaptive evolution has been rapid, likely fueled by high heritability, but long-distance movement may hinder adaptation by reducing the heritable genetic variation on which selection can act.
BOLA3 and NFU1 link mitoribosome iron–sulfur cluster assembly to multiple mitochondrial dysfunctions syndrome

The human mitochondrial ribosome contains three [2Fe-2S] clusters whose assembly pathway, role, and implications for mitochondrial and metabolic diseases are unknown. Here, structure-function correlation studies show that the clusters play a structural role during mitoribosome assembly. To uncover the assembly pathway, we have examined the effect of silencing the expression of Fe-S cluster biosynthetic and delivery factors on mitoribosome stability. We find that the mitoribosome receives its [2Fe-2S] clusters from the GLRX5-BOLA3 node. Additionally, the assembly of the small subunit depends on the mitoribosome biogenesis factor METTL17, recently reported containing a [4Fe-4S] cluster, which we propose is inserted via the ISCA1-NFU1 node. Consistently, fibroblasts from subjects suffering from “multiple mitochondrial dysfunction” syndrome due to mutations in BOLA3 or NFU1 display previously unrecognized attenuation of mitochondrial protein synthesis that contributes to their cellular and pathophysiological phenotypes. Finally, we report that, in addition to their structural role, one of the mitoribosomal [2Fe-2S] clusters and the [4Fe-4S] cluster in mitoribosome assembly factor METTL17 sense changes in the redox environment, thus providing a way to regulate organellar protein synthesis accordingly.
ULDNA: Integrating Unsupervised Multi-Source Language Models with LSTM-Attention Network for Protein-DNA Binding Site Prediction

Accurate identification of protein-DNA interactions is critical to understand the molecular mechanisms of proteins and design new drugs. We proposed a novel deeplearning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases. The method was systematically tested on 1287 proteins with DNA-binding site annotation from Protein Data Bank. Experimental results showed that ULDNA achieved a significant increase of the DNA-binding site prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the major advantage of ULDNA lies in the utilization of three pre-trained transformer language models which can extract the complementary DNA-binding patterns buried in evolution diversity-based feature embeddings in residue-level. Meanwhile, the designed LSTM-attention network could further enhance the correlation between evolution diversity and protein-DNA interaction. These results demonstrated a new avenue for high-accuracy deep-learning DNA-binding site prediction that is applicable to large-scale protein-DNA binding annotation from sequence alone.
Insights from Unsupervised Clustering and Composite Spectral Analysis Into the Physical Properties Driving Emission and Absorption in Quasar UV/Optical Spectra

Although quasar spectra appear remarkably similar, subtle systematic differences are wellknown and are linked to the physical conditions in the emitting regions. This thesis characterizes the properties of quasar optical and UV spectral features and reveals some of their underlying physical causes by analyzing large spectroscopic datasets from the Sloan Digital Sky Survey. We first examine the narrow-line region using four optical emission lines with a range of ionization potentials (IPs) using a sample of 16,027 quasars with redshift z ≤ 0.75. We bin the sample using the continuum luminosity and the Hβ width to constrain the accretion rates and black hole masses and create composite spectra from objects in each bin. The luminosities of the high IP lines are constant with redshift consistent with no evolution in the quasar spectral energy distribution (SED) or the host galaxy ISM illuminated by the continuum. In contrast, the [O II] line becomes stronger at high er redshifts pointing to an enhanced star formation contributing to the [O II] at higher redshifts. In the UV, we explore using unsupervised clustering (specifically the K-means algorithm) in searching for structure in the parameter space of quasar spectral measurements. We use measurements of 4,110 objects from the Pâris et al. (2014) Catalog and perform K-means on the equivalent widths (EWs), and the redand blue-half-width at half-maxima of Mg II, C IV, and C III]and use objects in each of the clusters to generate composite spectra. We find that quasars with larger C IV blueshifts and smaller EWs have softer ionizing radiation. This result is particularly interesting when seen using C III]. Next, we use a sample of 2,683 broad absorption line quasars from the Gibson et al. (2009) catalog. We apply K-means using the EWs and minimum and maximum outflow velocities of the C IV absorption trough and generate composite spectra from objects in each of the clusters. We find that objects with deep and broad C IV troughs are more likely to be variable and to show absorption from lower ionization transitions. Conversely, objects with shallower C IV troughs are less likely to be variable and have lower fractions of absorption from lower ionization lines. We examine this behaviour in light of properties such as SED hardness and intrinsic reddening.
The Use of Parallel Polynomial Preconditioners in the Solution of Systems of Linear Equations

This thesis mainly explores the use of polynomial preconditioners in iterative solvers for large-scale sparse linear systems Ax = b. It is well known that preconditioners can significantly improve the convergence of solvers, particularly when the coefficient matrix is ill-conditioned. Further, polynomial preconditioners have several advantages over other popular preconditioners — they may be implemented easily, they are highly parallel, and they are extremely agile. Due to the intrinsic disadvantages of polynomial methods (e.g., spectrum information is needed, poor stability of the large-degree polynomial preconditioning) and the limitation of computing technologies, the polynomial preconditioning technique was somehow ignored in the past ten years. Fortunately, at present, polynomial preconditioners are attracting more and more attention with the development of computer science. The construction of appropriate polynomial preconditioners can be transformed into constrained optimization problem, namely that of finding am-degree polynomial in matrix A, Pm(A), such that Pm(A) ≈ A. Three typical polynomial preconditioners arise, Neumann-series, Least-squares and Minimax. In this thesis Symmetric linear equations will be mainly discussed, where the spectrum of the coefficient matrix is expressed by a real compact set. In the case of SID (Symmetric Indefinite) linear systems, only Least-squares and Chebyshev methods are applicable. The implementation and detailed performance analysis of the Generalized Least-squares and the Generalized Minimax preconditioners are the main effort of this work. The convergence property of polynomial preconditioned iterative solvers is closely dependent on an accurate estimate of the eigenspectrum of the coefficient matrix. Consequently, it is natural to combine polynomial preconditioners with GMRES, the well-known standard algorithm for the computation of the solution of systems of linear equations which incorporates the so-called Arnoldi process. Thus, the use
Differences and similarities in personality functioning across different types of eating disorders

Objective The classification of anorexia nervosa (AN) into subtypes is relevant due to their different symptomatology. However, subtypes (restricting type: AN-R; purging type: AN-P) differ also in terms of their personality functioning. Knowledge about these differences would allow for better treatment stratification. A pilot study indicated differences in structural abilities that can be assessed by the operationalized psychodynamic diagnosis (OPD) system. The aim of this study was therefore to systematically explore differences in personality functioning and personality between the two AN subtypes and bulimia nervosa (BN) using three personality (functioning) constructs. Methods A total of N = 110 inpatients with AN-R (n = 28), AN-P (n = 40), or BN (n = 42) were recruited in three clinics for psychosomatic medicine. Assignment to the three groups was performed using a comprehensive questionnaire validated for diagnostic purposes (Munich-ED-Quest). Personality functioning was examined using OPD Structure Questionnaire (OPD-SQ), personality by using the Personality Inventory for DSM-5–Brief Form and Big Five Inventory-10. (M)ANOVAs were used to examine differences across eating disorder groups. In addition, correlation and regression analyses were conducted. Results We observed differences on several sub- and main scales of the OPD-SQ. Whereas patients with BN showed the lowest levels, AN-R patients displayed the highest levels of personality functioning. On some sub- and main scales, such as “affect tolerance,” the subtypes of AN differed from BN, whereas on the scale “affect differentiation,” AN-R, differed from the other two groups. The total eating disorder pathology score of the Munich-ED-Quest best predicted overall personality structure [stand. β = 0.650; t(104) = 6.666; p < 0.001] and self-regulation [stand. β = 0.449; t(104) = 3.628; p < 0.001]. Discussion Our findings confirm most of the results of the pilot study. These findings can facilitate the development of stratified treatment approaches for eating disorders.
Evidence of aerosol transmission of African swine fever virus between two piggeries under field conditions: a case study

African swine fever (ASF) is a devastating and economically significant infectious disease that has caused enormous losses in the commercial pig sector in China since 2018. The primary transmission routes of the African swine fever virus (ASFV), the causative agent of ASF, are direct pig-to-pig contact or indirect contact with virus-contaminated objects. While aerosol transmission of ASFV has been previously reported under experimental conditions, no reports have described it under field conditions. In this case study, aerosol-associated samples were collected over a monitoring period of 24 days in an ASFV-positive farm. A complete and clear chain of ASFV transmission through aerosols was observed: pigs in Room A on Day 0-aerosol in Room A on Day 6-dust of air outlets in Room A on Day 9-outdoor aerosols on Day 9-dust of air inlets in Room B on Day 15-aerosols/pigs in Room B on Day 21. Furthermore, a fluorescent powder experiment confirmed the transmission of dust from Room A to Room B. This study represents the first report providing evidence of aerosol transmission of ASFV under field conditions. Further research is needed to study the laws of aerosol transmission in ASFV and develop effective strategies such as air filtration or disinfection to create a low-risk environment with fresh air for pig herds.
The effect of soil microplastics on Oryza sativa L. root growth traits under alien plant invasion

Invasive alien plants pose severe threats to agroecosystems. Microplastic (MP) contamination in farmland soil is also concerning, as it causes crop stress and reduces productivity. However, the effects of the interactions between invasive alien plants and MP in the soil impact crops remain unclear. Herein, belowground plant characteristics associated with stress responses were examined in a pot experiment using root scan analyzes of rice plants exposed to Solidago canadensis L. invasion, polyethylene MP contamination, and a combined treatment. The observed changes in root growth traits under Canada goldenrod (Solidago canadensis L.) invasion were the least adverse, whereas S. canadensis invasion combined with soil MP contamination had the most adverse effects on root growth. Solidago canadensis L. invasion increased all belowground indices except root height and mean root diameter, which was upregulated in the soil MP contamination treatment. The combined treatment (S. canadensis invasion and soil MP contamination) reduced the belowground root growth traits more than the other treatments. The root growth traits may have been affected by changes in the antioxidant enzyme activity of the roots caused by the treatments. The combined effects of S. canadensis invasion and MP toxicity on rice root growth traits raise concerns regarding potential yields, financial damage, and consequences related to a potential move into the food web.
Intensity discrimination and neural representation of a masked tone in the presence of three types of masking release

Introduction Hearing ability is usually evaluated by assessing the lowest detectable intensity of a target sound, commonly referred to as a detection threshold. Detection thresholds of a masked signal are dependent on various auditory cues, such as the comodulation of the masking noise, interaural differences in phase, and temporal context. However, considering that communication in everyday life happens at sound intensities well above the detection threshold, the relevance of these cues for communication in complex acoustical environments is unclear. Here, we investigated the effect of three cues on the perception and neural representation of a signal in noise at supra-threshold levels. Methods First, we measured the decrease in detection thresholds produced by three cues, referred to as masking release. Then, we measured just-noticeable difference in intensity (intensity JND) to quantify the perception of the target signal at supra-threshold levels. Lastly, we recorded late auditory evoked potentials (LAEPs) with electroencephalography (EEG) as a physiological correlate of the target signal in noise at supra-threshold levels. Results The results showed that the overall masking release can be up to around 20 dB with a combination of these three cues. At the same supra-threshold levels, intensity JND was modulated by the masking release and differed across conditions. The estimated perception of the target signal in noise was enhanced by auditory cues accordingly, however, it did not differ across conditions when the target tone level was above 70 dB SPL. For the LAEPs, the P2 component was more closely linked to the masked threshold and the intensity discrimination than the N1 component. Discussion The results indicate that masking release affects the intensity discrimination of a masked target tone at supra-threshold levels, especially when the physical signal-to-noise is low, but plays a less significant role at high signal-to-noise ratios.
Hydrological forecasting at impact scale: the integrated ParFlow hydrological model at 0.6 km for climate resilient water resource management over Germany

In the context of the repeated droughts that have affected central Europe over the last years (2018–2020, 2022), climate-resilient management of water resources, based on timely information about the current state of the terrestrial water cycle and forecasts of its evolution, has gained an increasing importance. To achieve this, we propose a new setup for simulations of the terrestrial water cycle using the integrated hydrological model ParFlow/CLM at high spatial and temporal resolution (i.e., 0.611 km, hourly time step) over Germany and the neighboring regions. We show that this setup can be used as a basis for a monitoring and forecasting system that aims to provide stakeholders from many sectors, but especially agriculture, with diagnostics and indicators highlighting different aspects of subsurface water states and fluxes, such as subsurface water storage, seepage water, capillary rise, or fraction of plant available water for different (root-)depths. The validation of the new simulation setup with observation-based data monthly over the period 2011–2020 yields good results for all major components of the terrestrial water cycle analyzed here, i.e., volumetric soil moisture, evapotranspiration, water table depth, and river discharge. As this setup relies on a standardized grid definition and recent globally available static fields and parameters (e.g., topography, soil hydraulic properties, land cover), the workflow could easily be transferred to many regions of the Earth, including sparsely gauged regions, since ParFlow/CLM does not require calibration.
Associations of timing of food intake with energy intake, eating behaviour traits and psychosocial factors in adults with overweight and obesity

Introduction Whether a late distribution of food intake impacts obesity through increased energy intake remains uncertain and the behavioural characterization of late eating needs to be further investigated. The first objective of this study was to assess the associations between late eating and body mass index (BMI) and total energy intake (TEI), and whether TEI mediates the association between late eating and BMI. The second objective was to assess the associations between late eating and eating behaviour traits or psychosocial factors and whether eating behaviour traits mediate the association between late eating and TEI. Methods Baseline data from 301 individuals (56% women, age = 38.7 ± 8.5 years; BMI = 33.2 ± 3.4 kg/m2), who participated in four weight loss studies were used in this cross-sectional study. Total energy intake was assessed using a three-day food record from which the percentage of TEI after 17:00 and after 20:00 was calculated. Eating behaviour traits and psychosocial factors were assessed with questionnaires. Pearson correlations and mediation analyses adjusted for age, sex, underreporting of energy intake, sleep duration and bedtime were performed. Results Percent TEI after 17:00 and after 20:00 were associated with TEI (r = 0.13, p = 0.03 for both), and TEI mediated the association between percent TEI after 17:00 and BMI (β = 0.01 ± 0.01, 95% CI: 0.001, 0.02). Percent TEI after 17:00 was associated with disinhibition (r = 0.13, p = 0.03) and percent TEI after 20:00 was associated with susceptibility to hunger (r = 0.13, p = 0.03), stress (r = 0.24, p = 0.002) and anxiety (r = 0.28, p = 0.0004). In women, disinhibition mediated the association between percent TEI after 17:00 and TEI (β = 3.41 ± 1.43, 95% CI: 0.92, 6.47). Susceptibility to hunger mediated the association between percent TEI after 20:00 and TEI (β = 0.96 ± 0.59, 95% CI: 0.02, 2.34) in men and women. Conclusion Late eating is associated with TEI and suboptimal eating behaviours which could contribute to explaining the association between timing of food intake and obesity.
SSTM-IS: simplified STM method based on instance selection for real-time EEG emotion recognition

Introduction EEG signals can non-invasively monitor the brain activities and have been widely used in brain-computer interfaces (BCI). One of the research areas is to recognize emotions objectively through EEG. In fact, the emotion of people changes over time, however, most of the existing affective BCIs process data and recognize emotions offline, and thus cannot be applied to real-time emotion recognition. Methods In order to solve this problem, we introduce the instance selection strategy into transfer learning and propose a simplified style transfer mapping algorithm. In the proposed method, the informative instances are firstly selected from the source domain data, and then the update strategy of hyperparameters is also simplified for style transfer mapping, making the model training more quickly and accurately for a new subject. Results To verify the effectiveness of our algorithm, we carry out the experiments on SEED, SEED-IV and the offline dataset collected by ourselves, and achieve the recognition accuracies up to 86.78%, 82.55% and 77.68% in computing time of 7s, 4s and 10s, respectively. Furthermore, we also develop a real-time emotion recognition system which integrates the modules of EEG signal acquisition, data processing, emotion recognition and result visualization. Discussion Both the results of offline and online experiments show that the proposed algorithm can accurately recognize emotions in a short time, meeting the needs of real-time emotion recognition applications.