Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary history influences the microbiomes of a female symbiotic reproductive organ in cephalopods

ABSTRACT Many female squids and cuttlefishes have a symbiotic reproductive organ called the accessory nidamental gland (ANG) that hosts a bacterial consortium involved with egg defense against pathogens and fouling organisms. While the ANG is found in multiple cephalopod families, little is known about the global microbial diversity of these ANG bacterial symbionts. We used 16S rRNA gene community analysis to characterize the ANG microbiome from different cephalopod species and assess the relationship between host and symbiont phylogenies. The ANG microbiome of 11 species of cephalopods from four families (superorder: Decapodiformes) that span seven geographic locations was characterized. Bacteria of class Alphaproteobacteria, Gammaproteobacteria, and Flavobacteriia were found in all species, yet analysis of amplicon sequence variants by multiple distance metrics revealed a significant difference between ANG microbiomes of cephalopod families (weighted/unweighted UniFrac, Bray–Curtis, P = 0.001). Despite being collected from widely disparate geographic locations, members of the family Sepiolidae (bobtail squid) shared many bacterial taxa including (~50%) Opitutae (Verrucomicrobia) and Ruegeria (Alphaproteobacteria) species. Furthermore, we tested for phylosymbiosis and found a positive correlation between host phylogenetic distance and bacterial community dissimilarity (Mantel test r = 0.7). These data suggest that closely related sepiolids select for distinct symbionts from similar bacterial taxa. Overall, the ANGs of different cephalopod species harbor distinct microbiomes and thus offer a diverse symbiont community to explore antimicrobial activity and other functional roles in host fitness. IMPORTANCE Many aquatic organisms recruit microbial symbionts from the environment that provide a variety of functions, including defense from pathogens. Some female cephalopods (squids, bobtail squids, and cuttlefish) have a reproductive organ called the accessory nidamental gland (ANG) that contains a bacterial consortium that protects eggs from pathogens. Despite the wide distribution of these cephalopods, whether they share similar microbiomes is unknown. Here, we studied the microbial diversity of the ANG in 11 species of cephalopods distributed over a broad geographic range and representing 15–120 million years of host divergence. The ANG microbiomes shared some bacterial taxa, but each cephalopod species had unique symbiotic members. Additionally, analysis of host–symbiont phylogenies suggests that the evolutionary histories of the partners have been important in shaping the ANG microbiome. This study advances our knowledge of cephalopod–bacteria relationships and provides a foundation to explore defensive symbionts in other systems.
Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images

Many deep learning based methods have been proposed for brain tumor segmentation. Most studies focus on deep network internal structure to improve the segmentation accuracy, while valuable external information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists often screen lesion regions with normal appearance as reference in mind, in this paper, we propose a novel deep framework for brain tumor segmentation, where normal brain images are adopted as reference to compare with tumor brain images in a learned feature space. In this way, features at tumor regions, i.e., tumor-related features, can be highlighted and enhanced for accurate tumor segmentation. It is known that routine tumor brain images are multimodal, while normal brain images are often monomodal. This causes the feature comparison a big issue, i.e., multimodal vs. monomodal. To this end, we present a new feature alignment module (FAM) to make the feature distribution of monomodal normal brain images consistent/inconsistent with multimodal tumor brain images at normal/tumor regions, making the feature comparison effective. Both public (BraTS2022) and in-house tumor brain image datasets are used to evaluate our framework. Experimental results demonstrate that for both datasets, our framework can effectively improve the segmentation accuracy and outperforms the state-of-the-art segmentation methods. Codes are available at https://github.com/hb-liu/Normal-Brain-Boost-Tumor-Segmentation.
Landscape Heterogeneity Explains the Genetic Differentiation of a Forest Bird across the Sino-Himalayan Mountains

Abstract Mountains are the world's most important centers of biodiversity. The Sino-Himalayan Mountains are global biodiversity hotspot due to their extremely high species richness and endemicity. Ample research investigated the impact of the Qinghai–Tibet Plateau uplift and Quaternary glaciations in driving species diversification in plants and animals across the Sino-Himalayan Mountains. However, little is known about the role of landscape heterogeneity and other environmental features in driving diversification in this region. We utilized whole genomes and phenotypic data in combination with landscape genetic approaches to investigate population structure, demography, and genetic diversity in a forest songbird species native to the Sino-Himalayan Mountains, the red-billed leiothrix (Leiothrix lutea). We identified 5 phylogeographic clades, including 1 in the East of China, 1 in Yunnan, and 3 in Tibet, roughly consistent with differences in song and plumage coloration but incongruent with traditional subspecies boundaries. Isolation-by-resistance model best explained population differentiation within L. lutea, with extensive secondary contact after allopatric isolation leading to admixture among clades. Ecological niche modeling indicated relative stability in the extent of suitable distribution areas of the species across Quaternary glacial cycles. Our results underscore the importance of mountains in the diversification of this species, given that most of the distinct genetic clades are concentrated in a relatively small area in the Sino-Himalayan Mountain region, while a single shallow clade populates vast lower-lying areas to the east. This study highlights the crucial role of landscape heterogeneity in promoting differentiation and provides a deep genomic perspective on the mechanisms through which diversity hotspots form.
River temperature forecasting: case study for Little Southwest Miramichi River (New Brunswick, Canada)

ABSTRACT River temperature models play an increasingly important role in the management of fisheries and aquatic resources. Among river temperature models, forecasting models remain relatively unused compared to water temperature simulation models. However, water temperature forecasting is extremely important for in-season management of fisheries, especially when short-term forecasts (a few days) are required. In this study, forecast and simulation models were applied to the Little Southwest Miramichi River (New Brunswick, Canada), where water temperatures can regularly exceed 25–29°C during summer, necessitating associated fisheries closures. Second- and third-order autoregressive models (AR2, AR3) were calibrated and validated using air temperature as the exogenous variable to predict minimum, mean and maximum daily water temperatures. These models were then used to predict river temperatures in forecast mode (1-, 2- and 3-day forecasts using real-time data) and in simulation mode (using only air temperature as input). The results showed that the models performed better when used to forecast rather than simulate water temperatures. The AR3 model slightly outperformed the AR2 in the forecasting mode, with root mean square errors (RMSE) generally between 0.87°C and 1.58°C. However, in the simulation mode, the AR2 slightly outperformed the AR3 model (1.25°C < RMSE < 1.90°C). One-day forecast models performed the best (RMSE ~ 1°C) and model performance decreased as time lag increased (RMSE close to 1.5°C after 3 days). The study showed that marked improvement in the modelling can be accomplished using forecasting models compared to water temperature simulations, especially for short-term forecasts. EDITOR M.C. Acreman ASSOCIATE EDITOR S. Huang
Water use efficiency and yield of cowpea and nutrient loss in lysimeter experiment under varying water table depth, irrigation schedules and irrigation method

Lysimeter experiment was conducted at Govind Ballabh Pant University of Agriculture & Technology, Pantnagar during summer season 2013 to study the effect of irrigation schedules and methods on yield, nutrient uptake and water use efficiency of cowpea as well as nutrient loss from silty clay loam soil under fluctuating water table conditions. The experiment was laid out in factorial randomized block design having three irrigation schedules at IW/CPE ratio of 0.3. 0.2 and 0.15 with two irrigation methods (flood and sprinkler) and at 30±1.5, 60±1.5 and 90±1.5 cm water tables replicated thrice. Maximum root length (129.4 cm) and root length density (0.395 cm/cm 3 ) were obtained when irrigation was scheduled at IW: CPE 0.3 associated with 30±1.5 cm water table depth using sprinkler method. Increase in water table depth and IW: CPE ratio decreased water use efficiency where IW: CPE 0.3 produced highest grain yield (1411.6 kg ha -1 ) with the WUE of 1.15 kg ha mm -1 . Significant nutrients uptake response was observed owing to variation in water table depth, irrigation schedules and methods. Analysis of lysimeter leached water showed that with deep drainage and more IW:CPE, leaching losses of N,P and K were more however water applied through sprinkler saved 20.1, 53.7 and 24.4% N, P and K, respectively, over flooded method. Irrigation given at IW: CPE 0.3 through sprinkler form at 60±1.5 cm water table depth favours the higher grain yield and nutrient uptake by crop whereas flooded irrigation with deep water table condition accelerated nutrient leaching. SAARC J. Agri. , 14(2): 46-55 (2016)
m6A-Mediated Upregulation of Imprinted in Prader–Willi Syndrome Induces Aberrant Apical–Basal Polarization and Oxidative Damage in RPE Cells

Purpose To reveal the clinical significance, pathological involvement and molecular mechanism of imprinted in Prader–Willi syndrome (IPW) in RPE anomalies that contribute to AMD. Methods IPW expression under pathological conditions were detected by microarrays and qPCR assays. In vitro cultured fetal RPE cells were used to study the pathogenicity induced by IPW overexpression and to analyze its upstream and downstream regulatory networks. Results We showed that IPW is upregulated in the macular RPE–choroid tissue of dry AMD patients and in fetal RPE cells under oxidative stress, inflammation and dedifferentiation. IPW overexpression in fetal RPE cells induced aberrant apical–basal polarization as shown by dysregulated polarized markers, disrupted tight and adherens junctions, and inhibited phagocytosis. IPW upregulation was also associated with RPE oxidative damages, as demonstrated by intracellular accumulation of reactive oxygen species, reduced cell proliferation, and accelerated cell apoptosis. Mechanically, N6-methyladenosine level of the IPW transcript regulated its stability with YTHDC1 as the reader. IPW mediated RPE features by suppressing MEG3 expression to sequester its inhibition on the AKT serine-threonine kinase (AKT)/mammalian target of rapamycin (mTOR) pathway. We also noticed that the mTOR inhibitor rapamycin suppresses the AKT/mTOR pathway to alleviate the IPW-induced RPE anomalies. Conclusions We revealed that IPW overexpression in RPE induces aberrant apical–basal polarization and oxidative damages, thus contributing to AMD progression. We also annotated the upstream and downstream regulatory networks of IPW in RPE. Our findings shed new light on the molecular mechanisms of RPE dysfunctions, and indicate that IPW blockers may be a promising option to treat RPE abnormalities in AMD.
Development of the Japanese version of the Ego‐Dissolution Inventory (EDI)

Abstract Aim Psychedelics have recently gained attention as potential therapeutic agents for various psychiatric disorders. Previous research has highlighted that a diminished sense of self, commonly termed “ego‐dissolution” is a pivotal feature of the psychedelic‐induced state. While the Ego‐Dissolution Inventory (EDI) is a widely acknowledged instrument for measuring this phenomenon, no Japanese version has been available. This study aimed to develop a Japanese version of the EDI. Methods We adhered to the “Guidelines for Best Practices in the Translation and Cultural Modification Process for Patient‐Reported Outcomes Instruments: Document from the ISPOR Committee on Translation and Cultural Modification” during our translation approach. Two Japanese psychiatrists independently conducted initial translations, and a consolidated version was achieved via mutual agreement. This version was then back‐translated to English and assessed by the original authors for consistency. The repetitive modification process was conducted in continuous dialogues with the original authors until they accepted the concluding back‐translated version. Results The finalized, approved back‐translated version of the EDI is presented in the accompanying figure. In addition, the authorized Japanese version of the EDI is included in the Appendix. Conclusions In this study, we successfully developed the Japanese version of the EDI. This instrument will assist in assessing ego‐dissolution experiences associated with psychedelic‐assisted therapy among Japanese speakers. Additional studies are necessary to evaluate the reliability and validity of this newly translated instrument.
Calcium cyanamide - a synoptic review of an environmentally benign fertiliser which enhances soil health

Abstract Calcium cyanamide was the first artificial nitrogen fertiliser to be manufactured industrially. It contains approximately 20% nitrogen and 50% calcium and is an environmentally benign way of providing these nutrients for field and protected crops. This fertiliser’s properties are reviewed in this paper; these include:- a gradual release of nitrogen, hence the rhizosphere remains in harmony with the requirements of plant roots; leaching and demineralisation are minimal, as a consequence nitrate pollution is limited; the combination of nitrogen and calcium raises the efficiency of fertiliser applications reducing the carbon-footprint of farming operations while at the same time increasing crop yields and quality; soil health is improved as a result of increasing microbial diversity and this helps to suppress the effects of soil borne pathogens. The use of calcium cyanamide is set into the context of needs for increasing food production, reducing the intensity with which resources are used, enhancing natural biodiversity and providing for environmental sustainability.
Spatial dependence and heterogeneity in ten years of fertility decline in Brazil: where, why and how fast

Knowledge, innovations and behaviors that influence fertility decisions spread over space through teaching/learning processes and often occur through imitation between areas that are closer to each other. In this study, I adopt a spatially referenced approach to take into account this proximity and to analyze why, where and how fast fertility has declined in Brazil. Using data based on the Brazilian Census I investigate the spatial and temporal patterns of fertility in Brazilian municipalities between 1991 and 2000, identify the "hot and cold spots" in which fertility is clustered, and evaluate the importance of relevant socio-demographic factors accounting for these patterns. I do this by estimating and comparing the efficiency of two favorite models: ordinary least squares with spatially correlated errors and geographically weighted regressions (GWR). The results indicate that the last provides the best estimates and demonstrates that the importance of each variable is dependent on its spatial distribution. In particular, the relevance of the fertility in neighboring areas as a predictor for local total fertility rates (TFR) provides initial evidence that fertility may, indeed, follow diffusion mechanisms. The analysis also shows that the endogenous level of fertility prevailing in 1991 is extremely significant to determine how fast fertility declined in ten years. Intriguingly, once this variable is considered in the model, improvements in education do not necessarily have a negative impact on fertility.
The TESS–Keck Survey. XIX. A Warm Transiting Sub-Saturn-mass Planet and a Nontransiting Saturn-mass Planet Orbiting a Solar Analog

The Transiting Exoplanet Survey Satellite (TESS) continues to increase dramatically the number of known transiting exoplanets, and is optimal for monitoring bright stars amenable to radial velocity (RV) and atmospheric follow-up observations. TOI-1386 is a solar-type (G5V) star that was detected via TESS photometry to exhibit transit signatures in three sectors with a period of 25.84 days. We conducted follow-up RV observations using Keck/High Resolution Echelle Spectrometer (HIRES) as part of the TESS–Keck Survey, collecting 64 RV measurements of TOI-1386 with the HIRES spectrograph over 2.5 yr. Our combined fit of the TOI-1386 photometry and RV data confirm the planetary nature of the detected TESS signal, and provide a mass and radius for planet b of 0.148 ± 0.019 M J and 0.540 ± 0.017 R J, respectively, marking TOI-1386 b as a warm sub-Saturn planet. Our RV data further reveal an additional outer companion, TOI-1386 c, with an estimated orbital period of 227.6 days and a minimum mass of 0.309 ± 0.038 M J. The dynamical modeling of the system shows that the measured system architecture is long-term stable, although there may be substantial eccentricity oscillations of the inner planet due to the dynamical influence of the outer planet.
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization

Vision Transformers (ViTs) have gained prominence as a preferred choice for a wide range of computer vision tasks due to their exceptional performance. However, their widespread adoption has raised concerns about security in the face of malicious attacks. Most existing methods rely on empirical adjustments during the training process, lacking a clear theoretical foundation. In this study, we address this gap by introducing SpecFormer, specifically designed to enhance ViTs' resilience against adversarial attacks, with support from carefully derived theoretical guarantees. We establish local Lipschitz bounds for the self-attention layer and introduce a novel approach, Maximum Singular Value Penalization (MSVP), to attain precise control over these bounds. We seamlessly integrate MSVP into ViTs' attention layers, using the power iteration method for enhanced computational efficiency. The modified model, SpecFormer, effectively reduces the spectral norms of attention weight matrices, thereby enhancing network local Lipschitzness. This, in turn, leads to improved training efficiency and robustness. Extensive experiments on CIFAR and ImageNet datasets confirm SpecFormer's superior performance in defending against adversarial attacks.
The Information of Large Language Model Geometry

This paper investigates the information encoded in the embeddings of large language models (LLMs). We conduct simulations to analyze the representation entropy and discover a power law relationship with model sizes. Building upon this observation, we propose a theory based on (conditional) entropy to elucidate the scaling law phenomenon. Furthermore, we delve into the auto-regressive structure of LLMs and examine the relationship between the last token and previous context tokens using information theory and regression techniques. Specifically, we establish a theoretical connection between the information gain of new tokens and ridge regression. Additionally, we explore the effectiveness of Lasso regression in selecting meaningful tokens, which sometimes outperforms the closely related attention weights. Finally, we conduct controlled experiments, and find that information is distributed across tokens, rather than being concentrated in specific"meaningful"tokens alone.
SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning

In this paper, we perform a non-asymptotic analysis of the federated linear stochastic approximation (FedLSA) algorithm. We explicitly quantify the bias introduced by local training with heterogeneous agents, and investigate the sample complexity of the algorithm. We show that the communication complexity of FedLSA scales polynomially with the desired precision $\epsilon$, which limits the benefits of federation. To overcome this, we propose SCAFFLSA, a novel variant of FedLSA, that uses control variates to correct the bias of local training, and prove its convergence without assumptions on statistical heterogeneity. We apply the proposed methodology to federated temporal difference learning with linear function approximation, and analyze the corresponding complexity improvements.
Pre-training of Lightweight Vision Transformers on Small Datasets with Minimally Scaled Images

Can a lightweight Vision Transformer (ViT) match or exceed the performance of Convolutional Neural Networks (CNNs) like ResNet on small datasets with small image resolutions? This report demonstrates that a pure ViT can indeed achieve superior performance through pre-training, using a masked auto-encoder technique with minimal image scaling. Our experiments on the CIFAR-10 and CIFAR-100 datasets involved ViT models with fewer than 3.65 million parameters and a multiply-accumulate (MAC) count below 0.27G, qualifying them as 'lightweight' models. Unlike previous approaches, our method attains state-of-the-art performance among similar lightweight transformer-based architectures without significantly scaling up images from CIFAR-10 and CIFAR-100. This achievement underscores the efficiency of our model, not only in handling small datasets but also in effectively processing images close to their original scale.
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification

Making inferences in text comprehension to understand the meaning is essential in language processing. This work studies the entailment verification (EV) problem of multi-sentence premises that requires a system to make multiple inferences implicitly. Studying EV for such complex premises is important because modern NLP problems, such as detecting inconsistent model-generated rationales, require complex multi-hop reasoning. However, current textual inference datasets mostly contain short premises that only partially focus on these challenges. To address this, we compile an EV benchmark that includes datasets from three NLP domains (NLI, contextual QA, and rationales) containing multi-sentence premises. On benchmarking humans and LLMs, we find that LLMs are better than humans in multi-hop reasoning across extended contexts, while humans perform better in simple deductive reasoning tasks. We also finetune a Flan-T5 model for EV using two training objectives to obtain a strong open-source model that outperforms GPT-3.5 and rivals GPT-4. Finally, we use this model to filter out inconsistent model-generated rationales in self-consistency decoding, resulting in a 6% accuracy improvement on average across three MCQ datasets.