Miracle-dz/UniFAR-FTUs huggingface dataset:
New phylogenetic insights into the African catfish families Mochokidae and Austroglanididae

Abstract Several hundred catfish species (order: Siluriformes) belonging to 11 families inhabit Africa, of which at least six families are endemic to the continent. Although four of those families are well‐known to belong to the ‘Big‐Africa clade’, no previous study has addressed the phylogenetic placement of the endemic African catfish family Austroglanididae in a comprehensive framework with molecular data. Furthermore, interrelationships within the ‘Big‐Africa clade’, including the most diverse family Mochokidae, remain unclear. This study was therefore designed to help reconstruct inter‐ and intrarelationships of all currently valid mochokid genera, to infer their position within the ‘Big Africa clade’ and to establish a first molecular phylogenetic hypothesis of the relationships of the enigmatic Austroglanididae within the Siluriformes. We assembled a comprehensive mitogenomic dataset comprising all protein coding genes and representing almost all recognized catfish families (N = 33 of 39) with carefully selected species (N = 239). We recovered the monophyly of the previously identified multifamily clades ‘Big Asia’ and ‘Big Africa’ and determined Austroglanididae to be closely related to Pangasiidae, Ictaluroidea and Ariidae. Mochokidae was recovered as the sister group to a clade encompassing Auchenoglanididae, Claroteidae, Malapteruridae and the African Schilbeidae, albeit with low statistical support. The two mochokid subfamilies Mochokinae and Chiloglanidinae as well as the chiloglanid tribe Atopochilini were recovered as reciprocally monophyletic. The genus Acanthocleithron forms the sister group of all remaining Mochokinae, although with low support. The genus Atopodontus is the sister group of all remaining Atopochilini. In contrast to morphological reconstructions, the monophyly of the genus Chiloglanis was strongly supported in our analysis, with Chiloglanis macropterus nested within a Chiloglanis sublineage encompassing only other taxa from the Congo drainage. This is an important result because the phylogenetic relationships of C. macropterus have been controversial in the past, and because we and other researchers assumed that this species would be resolved as sister to most or all other members of Chiloglanis. The apparent paraphyly of Synodontis with respect to Microsynodontis provided an additional surprise, with Synodontis punu turning out to be the sister group of the latter genus.
A novel method of hard palate reconstruction utilizing a combination of the buccal fat pad and palatal island flap

The palatal island flap is reliable for single‐staged reconstruction of select oral defects. However, fistula formation is a disruptive potential complication. The authors employed this technique in five patients and present a representative case of a 65‐year‐old female with a left‐sided palatal salivary neoplasm. The patient underwent resection and was reconstructed utilizing a combination of the buccal fat pad and palatal island flap. Four of the five patients healed uneventfully. One patient experienced partial loss of the marginal zone of the palatal island flap which successfully granulated and did not lead to an oroantral fistula. The representative patient recovered uneventfully. At 2 weeks, she felt well, with no evidence of fistula. The anterior palate demonstrated early mucosalization. We present the novel, combined use of the palatal island flap and buccal fat pad flap to create a two‐layer closure and describe its advantages for posterior palate reconstruction.
Towards More Human and Humane Testing: The Role of the Device Supplier Industry

In vivo testing has been the gold standard for preclinical drug development and toxicology. However, animal-based methods often lack human relevance and have a low predictability rate, not to mention the enormous ethical and financial concerns associated with their use. For instance, according to the US Congressional Budget Office (cbo.gov), it takes an average of 10.5 years to take a compound from the preclinical phase to the market, with a cost of US$1–US$2 billion. 90% of drugs that are tested in animals and enter clinical trials fail due to lack of safety and efficacy — this fact questions the significance of in vivo testing. Cells in culture can recapitulate certain aspects of physiology and disease, as well as indicate drug responses and toxicity. Thus, they represent a sophisticated human-relevant and humane alternative. With advances in the in vitro field, it is anticipated that confidence will be gained towards a move away from traditional in vivo models. Specialised supplier industries have been a driving force in the transition to non-animal research, by translating new approach methodologies into scalable products that have been adopted by the regulatory and testing industries — but we are still at the beginning. In this article, we introduce the perspective of the device supplier industry on the current challenges and opportunities surrounding the adoption of new in vitro methods, with the goal of promoting effective co-operation with scientists and other stakeholders. In addition, we highlight some examples of where non-animal approaches have been used in regulatory submissions, as well as listing some educational and training resources that can help when selecting the most appropriate assay.
Serum ferritin levels in previously untreated classical Hodgkin lymphoma: correlations and prognostic significance

Abstract Serum ferritin (SF) is frequently elevated in classical Hodgkin lymphoma (cHL). We report on its prognostic significance in an unselected series of 529 cHL patients treated with state-of-the-art therapy. Higher baseline levels correlated with markers of advanced/aggressive disease. SF levels were significantly higher in male and older patients, those with high body mass index and mixed cellularity histology. The strongest correlation was recorded between SF and complement reactive protein (CRP) levels. Gender-specific SF cutoffs which provided the best discrimination in terms of freedom from progression (FFP) were identified. In multivariate analysis elevated SF levels, advanced stage and high lactate dehydrogenase (LDH) were independent prognostic factors of inferior FFP. SF also appears to retain independent prognostic significance for progression-free survival (PFS) but not for overall survival (OS). In conclusion, SF levels in cHL reflect disease activity and are associated with adverse patient outcomes.
CRISPR/Cas9-mediated mutagenesis of VvbZIP36 promotes anthocyanin accumulation in grapevine (Vitis vinifera)

Abstract Anthocyanins are plant secondary metabolites that have a variety of biological functions, including pigmentation. The accumulation of anthocyanins is regulated by both transcriptional activators and repressors. Studies have shown that bZIP family members act primarily as positive regulators of anthocyanin biosynthesis, but there are few reports of negative regulation. Here, we report that a grapevine (Vitis vinifera) bZIP gene from group K, VvbZIP36, acts as a negative regulator of anthocyanin biosynthesis. Knocking out one allele of VvbZIP36 in grapevine with CRISPR/Cas9 promoted anthocyanin accumulation. Correlation analysis of transcriptome and metabolome data showed that a range of anthocyanin biosynthesis genes were activated in VvbZIP36 mutant plants relative to the wild type, resulting in the accumulation of related metabolites, including naringenin chalcone, naringenin, dihydroflavonols, and cyanidin-3-O-glucoside. The synthesis of stilbenes (α-viniferin), lignans, and some flavonols (including quercetin-3-O-rhamnoside, kaempferol-3-O-rhamnoside, and kaempferol-7-O-rhamnoside) was significantly inhibited, and several genes linked to their metabolism were downregulated in the mutant plants. In summary, our results demonstrate that VvbZIP36 is a negative regulator of anthocyanin biosynthesis that plays a role in balancing the synthesis of stilbenes (α-viniferin), lignans, flavonols, and anthocyanins.
Is loss avoidance differentially rewarding in adolescents versus adults? Differences in ventral striatum and anterior insula activation during the anticipation of potential monetary losses

ABSTRACT Avoiding loss is a crucial, adaptive guide to human behavior. While previous developmental research has primarily focused on gaining rewards, less attention has been paid to loss processing and its avoidance. In daily life, it is often unknown how likely an action will result in a loss, making the role of uncertainty in loss processing particularly important. By using functional magnetic resonance imaging, we investigated the influence of varying outcome probabilities (12%, 34%, and 67%) on brain regions implicated in loss processing (ventral striatum (VS), anterior insula (AI)) by comparing 28 adolescents (10–18 years) and 24 adults (22–32 years) during the anticipation of potential monetary loss. Overall, results revealed slower RTs in adolescents compared to adults with both groups being faster in the experimental (monetary condition) vs. control trials (verbal condition). Fastest RTs were observed for the 67% outcome probability in both age groups. An age group × outcome probability interaction effect revealed the greatest differences between the groups for the 12% vs. the 67% outcome probability. Neurally, both age groups demonstrated a higher percent signal change in the VS and AI during the anticipation of potential monetary loss versus the verbal condition. However, adults demonstrated an even greater activation of VS and AI than adolescents during the anticipation of potential monetary loss, but not during the verbal condition. This may indicate that adolescents differ from adults regarding their experience of avoiding losing monetary rewards.
Using the Child Sheehan Disability Scale to Differentiate Severity Level in Youth With Anxiety Disorders and Obsessive Compulsive Disorder

The current study extends the psychometric support for the Child Sheehan Disability Scale (CSDS) as a measure of impairment associated with childhood anxiety disorders, including obsessive compulsive disorder. The CSDS was completed by 1,481 predominately Caucasian youth (55.4% female) ages 8 to 17 (M = 12.68, SD = 2.78) from primarily two-parent households and a parent across community, outpatient, intensive outpatient treatment, and residential settings. The results replicated and extended the previously found strong convergent validity, discriminant validity, and treatment sensitivity with a revised parent-report item in the larger sample. Moreover, the CSDS successfully differentiated between patients receiving treatment of different levels of intensity. These data were used to develop preliminary qualitative descriptors associating individual scores with a likely level of indicated treatment to enhance the clinical applicability of the CSDS. This study establishes the CSDS as one of the briefest and most rigorously evaluated measures of impairment associated with child anxiety. However, the performance of the CSDS must be examined in more representative samples before being applied to diverse populations.
Parsed Categoric Encodings with Automunge

The Automunge open source python library platform for tabular data pre-processing automates feature engineering data transformations of numerical encoding and missing data infill to received tidy data on bases fit to properties of columns in a designated train set for consistent and efficient application to subsequent data pipelines such as for inference, where transformations may be applied to distinct columns in"family tree"sets with generations and branches of derivations. Included in the library of transformations are methods to extract structure from bounded categorical string sets by way of automated string parsing, in which comparisons between entries in the set of unique values are parsed to identify character subset overlaps which may be encoded by appended columns of boolean overlap detection activations or by replacing string entries with identified overlap partitions. Further string parsing options, which may also be applied to unbounded categoric sets, include extraction of numeric substring partitions from entries or search functions to identify presence of specified substring partitions. The aggregation of these methods into"family tree"sets of transformations are demonstrated for use to automatically extract structure from categoric string compositions in relation to the set of entries in a column, such as may be applied to prepare categoric string set encodings for machine learning without human intervention.
A Link Mixture Model for Spatio-temporal Infection Data, with Applications to the COVID Epidemic

Spatio-temporal models for infection counts generally follow themes of the broader disease mapping literature, but may need to address speciﬁc features of spatiotemporal infection data including considerable time ﬂuctuations (with epidemic phases) and spatial diﬀusion. Low order autoregression is a feature of several recent spatio-temporal studies of infection data, possibly with lags on both within area infections and on infections in adjacent areas. Many epidemic time series show a period of relatively stable infection levels (possibly characterized as endemicity), followed by a sudden sharp phase of increasing infection levels. After the epidemic peak there is a period of descending rates and return to stability. Hence one may seek to adapt the autoregressive scheme to these pronounced ﬂuctuations, with temporary departures from stationarity, but returning to stationarity as rates descend and infections resume endemic levels. We consider a mixture link model for infection counts that allows adaptivity to both explosive phases and static endemicity. Two case study applications involve COVID area-time data, one for 32 London boroughs since the start of the COVID epidemic, the other focusing on the epidemic phase in 144 area of South East England associated with the Delta variant.
Truncated Diffusion Probabilistic Models

Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data. We reveal that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a significantly smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations.
Massive MIMO With Dual-Polarized Antennas

This paper considers a single-cell massive MIMO (multiple-input multiple-output) system with dual-polarized antennas at both the base station and users. We study a channel model that includes the key practical aspects that arise when utilizing dual-polarization: channel cross-polar discrimination (XPD) and cross-polar correlations (XPC) at the transmitter and receiver. We derive the achievable uplink and downlink spectral efficiencies (SE) with and without successive interference cancellation (SIC) when using the linear minimum mean squared error (MMSE), zero-forcing (ZF), and maximum ratio (MR) combining/precoding schemes. The expressions depend on the statistical properties of the MMSE channel estimator obtained for the dual-polarized channel model. Closed-form uplink and downlink SE expressions for MR combining/precoding are derived. Using these expressions, we propose power-control algorithms that maximize the uplink and downlink sum SEs under uncorrelated fading but can be used to enhance performance also with correlated fading. We compare the SEs achieved in dual-polarized and uni-polarized setups numerically and evaluate the impact of XPD and XPC conditions. The simulations reveal that dual-polarized setups achieve 40-60% higher SEs and the gains remain also under severe XPD and XPC. Dual-polarized also systems benefit more from advanced signal processing that compensates for imperfections.
A path to precision cosmology: synergy between four promising late-universe cosmological probes

In the next decades, it is necessary to forge new late-universe cosmological probes to precisely measure the Hubble constant and the equation of state of dark energy simultaneously. In this work, we show that the four novel late-universe cosmological probes, 21 cm intensity mapping (IM), fast radio burst (FRB), gravitational wave (GW) standard siren, and strong gravitational lensing (SGL), are expected to be forged into useful tools in solving the Hubble tension and exploring dark energy. We propose that the synergy of them is rather important in cosmology. We simulate the 21 cm IM, FRB, GW, and SGL data based on the hypothetical observations of the Hydrogen Intensity and Real-time Analysis eXperiment, the Square Kilometre Array, the Einstein Telescope, and the Large Synoptic Survey Telescope, respectively. We find that the four probes have different parameter dependencies in cosmological constraints, so any combination of them can break the degeneracies and thus significantly improve the constraint precision. The joint 21 cm IM+FRB+GW+SGL data can provide the constraint errors of σ(Ωm) = 0.0022 and σ(H 0) = 0.16 km s-1 Mpc-1 in the ΛCDM model, which meet the standard of precision cosmology, i.e., the constraint precision of parameters is better than 1%. In addition, the joint data give σ(w) = 0.020 in the wCDM model, and σ(w 0) = 0.066 and σ(wa ) = 0.25 in the w 0 wa CDM model, which are better than the constraints obtained by the CMB+BAO+SN data. We show that the synergy between the four late-universe cosmological probes has magnificent prospects.
The detailed chemical abundance patterns of accreted halo stars from the optical to infrared

Understanding the assembly of our Galaxy requires us to also characterize the systems that helped build it. In this work, we accomplish this by exploring the chemistry of accreted halo stars from the Gaia-Enceladus/Gaia-Sausage (GES) selected in the infrared from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 16. We use high resolution optical spectra for 62 GES stars to measure abundances in 20 elements spanning the $\alpha$, Fe-peak, light, odd-Z, and notably, the neutron-capture groups of elements to understand their trends in the context of and in contrast to the Milky Way and other stellar populations. Using these derived abundances we find that the optical and the infrared abundances agree to within 0.15 dex except for O, Co, Na, Cu, and Ce. These stars have enhanced neutron-capture abundance trends compared to the Milky Way, and their [Eu/Mg] and neutron-capture abundance ratios (e.g., [Y/Eu], [Ba/Eu], [Zr/Ba], [La/Ba], and [Nd/Ba]) point to r-process enhancement and a delay in s-process enrichment. Their [$\alpha$/Fe] trend is lower than the Milky Way trend for [Fe/H]$>$-1.5 dex, similar to previous studies of GES stars and consistent with the picture that these stars formed in a system with a lower rate of star formation. This is further supported by their depleted abundances in Ni, Na, and Cu abundances, again, similar to previous studies of low-$\alpha$ stars with accreted origins.
Interacting Contour Stochastic Gradient Langevin Dynamics

We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equivalent computational budget. We also present a novel random-field function, which facilitates the estimation of self-adapting parameters in big data and obtains free mode explorations. Empirically, we compare the proposed algorithm with popular benchmark methods for posterior sampling. The numerical results show a great potential of ICSGLD for large-scale uncertainty estimation tasks.
Geodesic Quantum Walks

We propose a new family of discrete-spacetime quantum walks capable to propagate on any arbitrary triangulations. Moreover we also extend and generalize the duality principle introduced by one of the authors, linking continuous local deformations of a given triangulation and the inhomogeneity of the local unitaries that guide the quantum walker. We proved that in the formal continuous limit, in both space and time, this new family of quantum walks converges to the (1+2)D massless Dirac equation on curved manifolds. We believe that this result has relevance in both modelling/simulating quantum transport on discrete curved structures, such as fullerene molecules or dynamical causal triangulation, and in addressing fast and efficient optimization problems in the context of the curved space optimization methods.
DRL-based Joint Beamforming and BS-RIS-UE Association Design for RIS-Assisted mmWave Networks

Reconfigurable intelligent surface (RIS) is considered as an extraordinarily promising technology to solve the blockage problem of millimeter wave (mmWave) communications owing to its capable of establishing a reconfigurable wireless propagation. In this paper, we focus on a RIS-assisted mmWave communication network consisting of multiple base stations (BSs) serving a set of user equipments (UEs). Considering the BS-RIS-UE association problem which determines that the RIS should assist which BS and UEs, we joint optimize BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate of the system. To solve this intractable non-convex problem, we propose a soft actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming and BS-RIS-UE association design algorithm, which can learn the best policy by interacting with the environment using less prior information and avoid falling into the local optimal solution by incorporating with the maximization of policy information entropy. The simulation results demonstrate that the proposed SAC-DRL algorithm can achieve significant performance gains compared with benchmark schemes.
Multiscale Structure of Poly(ionic liquid)s in Bulk and Solutions by Small-Angle Neutron Scattering

Poly(ionic liquid)s (PILs), similar to their ionic liquid (IL) analogues, present a nanostructure arising from local interactions. The influence of this nanostructure on the macromolecular conformation of polymer chains is investigated for the first time by means of an extensive use of small angle neutron scattering on a series of poly(1-vinyl-3-alkylimidazolium)s with varying alkyl side-chain length and counter-anion both in bulk and in dilute solutions. Radii of gyration are found to increase with the side-chain length in solution as a consequence of crowding interactions between neighboring monomers. In bulk, however, a non monotonic evolution of the radius of gyration reflects a change in chain flexibility and a potential screening of electrostatic interactions. Additionally at smaller scale, SANS provides an experimental estimation of both the chain diameter and the correlation length between neighboring chains, comparison of which unveils clear evidence of interdigitation of the alkyl side-chains. These structural features bring precious insights in the understanding of the dynamic properties of PILs.