Miracle-dz/UniFAR-FTUs huggingface dataset:
Metabolic, Enzymatic Activity, and Transcriptomic Analysis Reveals the Mechanism Underlying the Lack of Characteristic Floral Scent in Apricot Mei Varieties

Apricot mei, a hybrid of Prunus mume and Prunus sibirica, usually has greater cold resistance than P. mume; however, most varieties of Apricot mei lack the characteristic floral scent of P. mume. The volatile and intracellular metabolites, activity levels of key enzymes, and transcriptomes of blooming flowers were comprehensively investigated in five varieties of P. mume. Benzyl acetate and eugenol were determined to be the main components of the P. mume floral scent. However, benzyl benzoate and benzyl alcohol benzoyltransferase activity was detected in only the low-fragrance varieties “Dan Fenghou” and “Yanxing. ” No benzyl alcohol or benzaldehyde reductase (BAR) activity was detected in the non-fragrant variety “Fenghou. ” PmBAR1 and PmBAR3 were identified as the key genes responsible for BAR activity. The lack of benzyl alcohol synthesis in the “Fenghou” variety was caused by low activity of PmBAR1-Fen and low expression of PmBAR3. The 60-aa segment at the N-terminus of PmBAR3 was found to play an important role in its enzymatic activity. Correlation tests between floral scent metabolites and the transcriptomes of the five different scented varieties showed that some transcripts associated with hormones, stresses, posttranslational modifications and transporters may also play important regulatory roles in floral scent metabolism in the different varieties.
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification

Many real-world physical control systems are required to satisfy constraints upon deployment. Furthermore, real-world systems are often subject to effects such as non-stationarity, wear-and-tear, uncalibrated sensors and so on. Such effects effectively perturb the system dynamics and can cause a policy trained successfully in one domain to perform poorly when deployed to a perturbed version of the same domain. This can affect a policy's ability to maximize future rewards as well as the extent to which it satisfies constraints. We refer to this as constrained model misspecification. We present an algorithm with theoretical guarantees that mitigates this form of misspecification, and showcase its performance in multiple Mujoco tasks from the Real World Reinforcement Learning (RWRL) suite.
A New Adaptive Entropy Portfolio Selection Model

In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz’s mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange 50 (SSE50) index constituent stocks data set. Our outcomes show, compared with the traditional portfolio selection model, that AEM tends to make our investments more decentralized and hence helps to neutralize unsystematic risks. Due to the existence of self-adaptation, AEM turns out to be more adaptable to market fluctuations and helps to maintain the balance between the decentralized and concentrated investments in order to meet investors’ expectations. Our model applies equally well to portfolio optimizations for other financial markets.
Changes in Tree Canopy, Groundcover, and Avian Community Following Restoration of a Montane Longleaf Pine Woodland

Abstract. Fire suppression and hardwood encroachment are two of the most significant threats to the imperiled, fire-dependent montane longleaf pine ecosystem. We examined the effects of restoration of a montane longleaf pine forest in Paulding County, Georgia, U.S.A. on tree canopy, groundcover and bird communities over a decade. The restoration included a program of prescribed fire and selective thinning to reduce tree canopy density and reduce or remove offsite species. Several conservation goals were met including the recovery of characteristic tree composition and groundcover. Birds responded with sharp increases in richness and abundance, with many shrub and woodland dependent species of high conservation value detected post-restoration. Our research demonstrates these sites are easily restorable and such projects will likely yield significant gains for conservation.
Establishment and characterization of fibroblast cultures derived from a female common hippopotamus (Hippopotamus amphibius) skin biopsy

The population decline in the common hippopotamus (Hippopotamus amphibius) has necessitated the preservation of their genetic resources for species conservation and research. Of all actions, cryopreservation of fibroblast cell cultures derived from animal biopsy is considered a simple but efficient means. Nevertheless, preserving viable cell cultures of the common hippopotamus has not been achieved to our knowledge. To this end, we detailed a method to establish fibroblast cell cultures from a female common hippopotamus fetus in this study. By combining the classic tissue explant direct culture and enzymatic digestion methods, we isolated a great number of cells with typical fibroblastic morphology and high viability. Characterization of the fibroblast cultures was carried out using different techniques. In short, neither bacteria/fungi nor mycoplasma was detectable in the cell cultures throughout the study. The population doubling time was 23.9 h according to the growth curve. Karyotyping based on Giemsa staining showed that cultured cells were diploid with 36 chromosomes in all, one pair of which was sex chromosomes. Mitochondrial cytochrome C oxidase subunit I gene sequence of the cultured cells was 99.26% identical with the Hippopotamus amphibius complete mitochondrial DNA sequence registered in GenBank, confirming the cells were derived from a common hippopotamus. Flow cytometry and immunofluorescence staining results revealed that the detected cells were positive for fibroblast markers, S100A4 and Vimentin. In conclusion, we isolated and characterized a new fibroblast cell culture from a common hippopotamus skin sample and the cryopreserved cells could be useful genetic materials for the future research.
Evaluation Framework for Context-aware Speaker Recognition in Noisy Smart Living Environments

The integration of voice control into connected devices is expected to improve the efficiency and comfort of our daily lives. However, the underlying biometric systems often impose constraints on the individual or the environment during interaction (e.g., quiet surroundings). Such constraints have to be surmounted in order to seamlessly recognize individuals. In this paper, we propose an evaluation framework for speaker recognition in noisy smart living environments. To this end, we designed a taxonomy of sounds (e.g., home-related, mechanical) that characterize representative indoor and outdoor environments where speaker recognition is adopted. Then, we devised an approach for off-line simulation of challenging noisy conditions in vocal audios originally collected under controlled environments, by leveraging our taxonomy. Our approach adds a (combination of) sound(s) belonging to the target environment into the current vocal example. Experiments on a large-scale public dataset and two state-of-the-art speaker recognition models show that adding certain background sounds to clean vocal audio leads to a substantial deterioration of recognition performance. In several noisy settings, our findings reveal that a speaker recognition model might end up to make unreliable decisions. Our framework is intended to help system designers evaluate performance deterioration and develop speaker recognition models more robust to smart living environments.
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata

Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix product states, a tensor-train decomposition for probabilistic modeling, motivated by the need to tractably model many-body systems. But similar models have also been studied in the stochastic processes and weighted automata literature, with little work on how these bodies of work relate to each other. We address this gap by showing how stationary or uniform versions of popular quantum tensor network models have equivalent representations in the stochastic processes and weighted automata literature, in the limit of infinitely long sequences. We demonstrate several equivalence results between models used in these three communities: (i) uniform variants of matrix product states, Born machines and locally purified states from the quantum tensor networks literature, (ii) predictive state representations, hidden Markov models, norm-observable operator models and hidden quantum Markov models from the stochastic process literature,and (iii) stochastic weighted automata, probabilistic automata and quadratic automata from the formal languages literature. Such connections may open the door for results and methods developed in one area to be applied in another.
Rapidly non-ipf progressive fibrosing interstitial lung disease: a phenotype with an ipf-like behavior

Background: A subgroup of patients with fibrotic ILD experience progression and several risk factors for ILD progression have been reported, such as male sex, older age, lower baseline pulmonary function, and a radiological or pathological pattern of usual interstitial pneumonia. Objective: To describe a possible new phenotype of rapidly non IPF progressive fibrosing with an IPF-like outcome. Methods: Three previously fit and well patients who developed a rapidly progressive ILD and died within 6 to 7 months from the initial development of respiratory symptoms. Results: Unlike what is currently known, our patients developed a severe fibrosing ILD with an IPF-like outcome despite a) being younger than the average patient with IPF, b) having received a non-IPF MDT diagnosis, c) having a non-UIP pattern on HRCT. Moreover and similarly to IPF, they failed to respond to immunosuppressive treatment which is the preferred treatment option in these cases. Conclusion: We believe that patients who present with similar characteristics should be considered as likely to develop a phenotype of rapidly progressive ILD and be treated with antifibrotic medications instead of immunosuppressive ones according to the favourable treatment response to antifibrotic therapy observed in clinical trials of patients with progressive fibrosing ILDs. (Sarcoidosis Vasc Diffuse Lung Dis 2020; 37 (2): 231-233)
Reinforcement Learning for Optimization of COVID-19 Mitigation policies

The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date,the even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic impact without overwhelming the hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; and (2) an RL-based methodology for optimizing fine-grained mitigation policies within this simulator. Our results validate both the overall simulator behavior and the learned policies under realistic conditions.
The chromatin-binding domain of Ki-67 together with p53 protects human chromosomes from mitotic damage

Significance Ki-67 is a clinically important tumor proliferation marker protein. Ki-67 contributes to the three-dimensional organization of heterochromatin during interphase of the cell cycle, and also coats and shapes condensed chromosomes during mitosis. Here, we show that acute depletion of Ki-67 in human cells causes DNA damage as cells traverse mitosis. When the tumor suppressor protein p53 is codepleted with Ki-67, multiple hallmarks of genome instability arise, including anaphase chromosome bridges, followed by the appearance of micronuclei. The C-terminal chromatin-binding domain of Ki-67 is sufficient for resistance to this damage. These studies uncover a factor for genome maintenance and identify a protective protein domain. Vertebrate mammals express a protein called Ki-67 which is most widely known as a clinically useful marker of highly proliferative cells. Previous studies of human cells indicated that acute depletion of Ki-67 can elicit a delay at the G1/S boundary of the cell cycle, dependent on induction of the checkpoint protein p21. Consistent with those observations, we show here that acute Ki-67 depletion causes hallmarks of DNA damage, and the damage occurs even in the absence of checkpoint signaling. This damage is not observed in cells traversing S phase but is instead robustly detected in mitotic cells. The C-terminal chromatin-binding domain of Ki-67 is necessary and sufficient to protect cells from this damage. We also observe synergistic effects when Ki-67 and p53 are simultaneously depleted, resulting in increased levels of chromosome bridges at anaphase, followed by the appearance of micronuclei. Therefore, these studies identify the C terminus of Ki-67 as an important module for genome stability.
Temporal changes in metabolism late in seed development affect biomass composition in soybean

The inverse correlation between protein and oil production in soybeans is well-documented; however, it has been based primarily on the composition of mature seeds. Though this is the cumulative result of events over the course of soybean seed development, it does not convey information specific to metabolic fluctuations during developmental growth regimes. Maternal nutrient supply via seed coat exudate measurements and metabolite levels within the cotyledon were assessed across development to identify trends in the accumulation of central carbon and nitrogen metabolic intermediates. Active metabolic operation during late seed development was probed through transient labeling with 13C substrates. The results indicated: i) a drop in lipid during seed maturation with a concomitant increase in carbohydrates, ii) a transition from seed filling to maturation phase characterized by quantitatively balanced changes in the carbon use and CO2 release, iii) changes in measured carbon and nitrogen resources supplied maternally over development, iv) 13C metabolites processed through gluconeogenesis towards sustained carbohydrate accumulation as the maternal nutrient supply diminishes, and v) oligosaccharide biosynthetic metabolism during seed coat senescence at maturation. These results highlight temporal engineering targets for altering final biomass composition to increase the value of soybeans and a path to breaking the inverse seed protein and oil correlation. One-sentence summary Assessment of temporal changes in metabolism during soybean seed development indicated that lipid turnover during maturation contributes carbon for gluconeogenic production of carbohydrates.
The viral protein NSP1 acts as a ribosome gatekeeper for shutting down host translation and fostering SARS-CoV-2 translation

SARS-CoV-2 coronavirus is responsible for Covid-19 pandemic. In the early phase of infection, the single-strand positive RNA genome is translated into non-structural proteins (NSP). One of the first proteins produced during viral infection, NSP1, binds to the host ribosome and blocks the mRNA entry channel. This triggers translation inhibition of cellular translation. In spite of the presence of NSP1 on the ribosome, viral translation proceeds however. The molecular mechanism of the so-called viral evasion to NSP1 inhibition remains elusive. Here, we confirm that viral translation is maintained in the presence of NSP1. The evasion to NSP1-inhibition is mediated by the cis-acting RNA hairpin SL1 in the 5’UTR of SARS-CoV-2. NSP1-evasion can be transferred on a reporter transcript by SL1 transplantation. The apical part of SL1 is only required for viral translation. We show that NSP1 remains bound on the ribosome during viral translation. We suggest that the interaction between NSP1 and SL1 frees the mRNA accommodation channel while maintaining NSP1 bound to the ribosome. Thus, NSP1 acts as a ribosome gatekeeper, shutting down host translation or fostering SARS-CoV-2 translation depending on the presence of the SL1 5’UTR hairpin. SL1 is also present and necessary for translation of sub-genomic RNAs in the late phase of the infectious program. Consequently, therapeutic strategies targeting SL1 should affect viral translation at early and late stages of infection. Therefore, SL1 might be seen as a genuine ‘Achille heel’ of the virus.
What makes multilingual BERT multilingual?

Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth experimental study to supplement the existing literature of cross-lingual ability. We compare the cross-lingual ability of non-contextualized and contextualized representation model with the same data. We found that datasize and context window size are crucial factors to the transferability.
Giant Planets, Tiny Stars: Producing Short-period Planets around White Dwarfs with the Eccentric Kozai–Lidov Mechanism

The recent discoveries of WD J091405.30+191412.25 (WD J0914 hereafter), a white dwarf (WD) likely accreting material from an ice-giant planet, and WD 1856+534 b (WD 1856 b hereafter), a Jupiter-sized planet transiting a WD, are the first direct evidence of giant planets orbiting WDs. However, for both systems, the observations indicate that the planets’ current orbital distances would have put them inside the stellar envelope during the red-giant phase, implying that the planets must have migrated to their current orbits after their host stars became WDs. Furthermore, WD J0914 is a very hot WD with a short cooling time that indicates a fast migration mechanism. Here, we demonstrate that the Eccentric Kozai–Lidov Mechanism, combined with stellar evolution and tidal effects, can naturally produce the observed orbital configurations, assuming that the WDs have distant stellar companions. Indeed, WD 1856 is part of a stellar triple system, being a distant companion to a stellar binary. We provide constraints for the orbital and physical characteristics for the potential stellar companion of WD J0914 and determine the initial orbital parameters of the WD 1856 system.
Quantitative assessment of NCLDV–host interactions predicted by co-occurrence analyses

Nucleocytoplasmic DNA viruses (NCLDVs) are highly diverse and abundant in marine environments. However, knowledge of their hosts is limited because only a few NCLDVs have been isolated. By taking advantage of the rapidly increasing metagenomic data, in silico host prediction approaches are expected to fill this gap between known virus–host relationships and the true but largely unknown amount of NCLDVs. In this study, we built co-occurrence networks between NCLDVs and eukaryotes using the Tara Oceans metagenome and metabarcoding datasets to predict virus–host interactions. Using the positive likelihood ratio to assess the performance of host prediction for NCLDVs, we demonstrated that co-occurrence approaches can increase the odds of predicting true positive relationships four-fold compared with random host predictions in the high-weight region (weight > 0.4). To refine the host predictions from high-dimensional co-occurrence networks, we employed a recently proposed phylogeny-based method, Taxon Interaction Mapper, and showed that Taxon Interaction Mapper further improved the prediction performance eight-fold using weight cut-off filtration (> 0.4). Finally, we inferred virophage and NCLDV networks that further corroborated that co-occurrence approaches are effective for predicting NCLDV hosts in marine environments. Importance NCLDVs infect a wide range of eukaryotes. Their life circle is less dependent on hosts than other viruses. However, our understanding of NCLDV–host systems is highly limited because few of these viruses have been isolated. Co-occurrence inference is a possible way to predict virus–host interactions. For the first time, we quantitatively assessed the effectiveness of co-occurrence analyses for NCLDV host prediction. We also improved the performance of these co-occurrence analyses with a phylogeny-guided filtering method and produced a list of candidate hosts for three NCLDV families.
Sum‐throughput maximization for overlay cognitive wireless powered network with energy harvesting capability

In this article, a novel time division multiple access (TDMA)‐based approach for optimal resource allocation in an overlay cognitive wireless powered communication network is proposed. In this network, power in the secondary network (SN) is provided by the primary network (PN) in addition to its own wireless network. Using the TDMA and a harvest‐then‐transmit protocol, the time and power allocation for downlink wireless energy transfer (WET) and uplink wireless information transmission (WIT) are jointly optimized to maximize the sum‐throughput in SN. Quality of service in PN and power and time in SN, as the main constrains are considered for sum‐throughput maximization in this network. This is a non‐convex problem which gets converted to a convex problem using active interference‐temperature control technique. Moreover, a new closed‐form expression which results in sub‐optimal power and time‐allocation (SO‐PT‐A) is addressed. The performance of proposed algorithm is compared with the equal time allocation (ETA) and fixed TDMA‐allocation (FTDMA‐A) algorithms which impose some special constraints for optimal power and time allocation. The simulation results depict that the SO‐PT‐A algorithm outperforms the ETA and FTDMA‐A algorithms. Eventually, the performance of the SO‐PT‐A algorithm is evaluated with other well‐known algorithms considering different values of the average and maximum transmit power of the hybrid‐access point (H‐AP), the number of secondary users (SUs) and path loss exponent.
Propagation of Polygonatum macranthum (Maxim.) Koidz. from immature seeds using a new sterilization procedure.

Natural seed germination is difficult to achieve in numerous plant species of wide economic importance. The germination of Polygonatum macranthum seeds takes as long as one and a half years under natural conditions. In addition, propagation by rhizome is also extremely slow in this species. Therefore, the natural propagation of P. macranthum through seeds or rhizome is not efficient. In this study, an efficient in vitro propagation system for P. macranthum from immature seeds with seed coat was developed, using a new surface sterilization protocol that utilized a low concentration of hypochlorite. In vitro germination was achieved at a rate of 30% within 9 weeks after inoculation on 1/2 MS medium. Shoot explants from seedlings were successfully cultured on 1/2 MS medium. Supplementation of the 1/2 MS medium with cytokinin 6-benzylaminopurine (BAP) facilitated efficient propagation by microrhizome. An efficient propagation rate of 1.3 microrhizomes per shoot in an 8-week culture period could be achieved by using a concentration of 1 mg l-1 BAP. During 4 weeks of acclimatization, 88% of shoots were rooted and started to grow into juvenile plants. After about 16 weeks in the field, 13% of the acclimatized plants showed viable growth and healthy regenerating shoots. The cultivation system demonstrated in this study can be used to propagate P. macranthum.