Miracle-dz/UniFAR-FTUs huggingface dataset:
Parsing propagule pressure: Number, not size, of introductions drives colonization success in a novel environment

Predicting whether individuals will colonize a novel habitat is of fundamental ecological interest and is crucial to both conservation efforts and invasive species management. A consistently supported predictor of colonization success is the number of individuals introduced, also called propagule pressure. Propagule pressure increases with the number of introductions and the number of individuals per introduction (the size of the introduction), but it is unresolved which process is a stronger driver of colonization success. Furthermore their relative importance may depend upon the environment, with multiple introductions potentially enhancing colonization of fluctuating environments. To evaluate the relative importance of the number and size of introductions and its dependence upon environmental variability, we paired demographic simulations with a microcosm experiment. Using Tribolium flour beetles as a model system, we introduced a fixed number of individuals into replicated novel habitats of stable or fluctuating quality, varying the number of introductions through time and size of each introduction. We evaluated establishment probability and the size of extant populations after 7 generations. In the simulations and microcosms, we found that establishment probability increased with more, smaller introductions, but was not affected by biologically realistic fluctuations in environmental quality. Population size was not significantly affected by environmental variability in the simulations, but populations in the microcosms grew larger in a stable environment, especially with more introduction events. In general, the microcosm experiment yielded higher establishment probability and larger populations than the demographic simulations. We suggest that genetic mechanisms likely underlie these differences and thus deserve more attention in efforts to parse propagule pressure. Our results highlight the importance of preventing further introductions of undesirable species to invaded sites, and suggest conservation efforts should focus on increasing the number of introductions or re-introductions of desirable species rather than increasing the size of those introduction events.
A monovalent ion in the DNA binding interface of the eukaryotic junction-resolving enzyme GEN1

Abstract GEN1 is a member of the FEN/EXO family of structure-selective nucleases that cleave 1 nt 3′ to a variety of branchpoints. For each, the H2TH motif binds a monovalent ion and plays an important role in binding one helical arm of the substrates. We investigate here the importance of this metal ion on substrate specificity and GEN1 structure. In the presence of K+ ions the substrate specificity is wider than in Na+, yet four-way junctions remain the preferred substrate. In a combination of K+ and Mg2+ second strand cleavage is accelerated 17-fold, ensuring bilateral cleavage of the junction. We have solved crystal structures of Chaetomium thermophilum GEN1 with Cs+, K+ and Na+ bound. With bound Cs+ the loop of the H2TH motif extends toward the active site so that D199 coordinates a Mg2+, buttressed by an interaction of the adjacent Y200. With the lighter ions bound the H2TH loop changes conformation and retracts away from the active site. We hypothesize this conformational change might play a role in second strand cleavage acceleration.
The Smallest Intestine (TSI) – a low volume in vitro model of the small intestine with increased throughput

Aims To develop a low volume in vitro model with increased throughput simulating the human small intestine, incorporating the presence of ileal microbiota, to study microbial behaviour in the small intestine. Methods and Results The Smallest Intestine in vitro model (TSI) was developed. During the simulated passage of the small intestine bile is absorbed by dialysis and pH adjusted to physiologically relevant level. A consortium of seven representative bacterial members of the ileum microbiota is included in the ileal stage of the model. It was found that the model can be applied for the testing of survival of probiotic bacteria in upper intestine. Moreover, it was found, that the presence of the ileal microbiota significantly impacted probiotic survival. Conclusions TSI allows testing a substantial number of samples, at low cost and short time, and is thus suitable as an in vitro screening platform. Multiples of five reactors can be added to increase the number of biological and technical replicates. Significance and Impact of the Study TSI serves as an alternative for costly and low throughput in vitro models currently existing on the market. Above that, significance of small intestine bacteria presence on survival of probiotic bacteria in the upper intestine was proved.
A fifty-year odyssey: prospects for a cytomegalovirus vaccine in transplant and congenital infection

ABSTRACT Introduction: It has been almost fifty years since the Towne strain was used by Plotkin and collaborators as the first vaccine candidate for cytomegalovirus (CMV). While that approach showed partial efficacy, there have been a multitude of challenges to improve on the promise of a CMV vaccine. Efforts have been dichotomized into a therapeutic vaccine for patients with CMV-infected allografts, either stem cells or solid organ, and a prophylactic vaccine for congenital infection. Areas covered: This review will evaluate research prospects for a therapeutic vaccine for transplant recipients that recognizes CMV utilizing primarily T cell responses. Similarly, we will provide an extensive discussion on attempts to develop a vaccine to prevent the manifestations of congenital infection, based on eliciting a humoral anti-CMV protective response. The review will also describe newer developments that have upended the efforts toward such a vaccine through the discovery of a second pathway of CMV infection that utilizes an alternative receptor for entry using a series of antigens that have been determined to be important for prevention of infection. Expert commentary: There is a concerted effort to unify separate therapeutic and prophylactic vaccine strategies into a single delivery agent that would be effective for both transplant-related and congenital infection.
BPT for Early-Onset Behavior Disorders: Examining the Link Between Treatment Components and Trajectories of Child Internalizing Symptoms

Behavioral Parent Training (BPT) is the standard of care for early-onset Behavior Disorders (BDs). Preliminary evidence suggests that BPT may also lead to improvement in comorbid symptomatology, particularly internalizing problems, in children with BDs, yet less is currently known about how BPT produces such cascading effects. To begin to address this gap in the literature, trajectory analyses were used to examine the link between treatment components of one mastery-based BPT program, Helping the Noncompliant Child (HNC), and child internalizing symptoms over the course of treatment. Findings revealed that parental use of the Attends skill (i.e., parental description of child activity with warmth and enthusiasm) over time was significantly associated with decreases in trajectories of child internalizing symptoms across treatment. Further probing of these effects revealed that parent use of average or above-average levels of Attends across treatment sessions led to significant reductions in child internalizing symptoms by Sessions 7 to 10 of treatment. Consistent with the movement toward a modular approach to the treatment of children, findings highlight the importance of identifying particular BPT skills that can be used in treatment to target multiple comorbid child symptom clusters. Clinical implications and future directions are discussed.
Early Hypertension and Diabetes after Living Kidney Donation: A National Cohort Study.

BACKGROUND Living kidney donors have an increased risk of end-stage renal disease, with hypertension and diabetes as the predominant causes. In this study, we sought to better understand the timeline when these diseases occur, focusing on the early postdonation period. METHODS We studied 41 260 living kidney donors in the US between 2008-2014 from the SRTR and modeled incidence rates and risk factors for hypertension and diabetes. RESULTS At 6-months, 1-year, and 2-years postdonation, there were 74, 162, and 310 cases of hypertension per 10 000 donors. Donors who were older (per 10 years, aIRR 1.40, 95% CI 1.29-1.51), male (aIRR 1.31, 95% CI 1.14-1.50), had higher BMI (per 5 units, aIRR 1.29, 95% CI 1.17-1.43), and were related to their recipient (first degree relative, aIRR 1.28, 95% CI 1.08-1.52; spouse, aIRR 1.34, 95% CI 1.08-1.66) were more likely to develop hypertension, while donors who were Hispanic/Latino were less likely (aIRR 0.71, 95% CI 0.55-0.93). At 6-months, 1-year, and 2-years, there were 2, 6, and 15 cases of diabetes per 10 000 donors. Donors who were older (per 10 years, aIRR 1.42, 95% CI 1.11-1.82), had higher BMI (per 5 units, aIRR 1.52, 95% CI 1.04-2.21), and were Hispanic/Latino (aIRR 2.45, 95% CI 1.14-5.26) were more likely to develop diabetes. CONCLUSIONS In this national study, new-onset diabetes was rare, but 3% of donors developed hypertension within 2 years of nephrectomy. These findings reaffirm that disease pathways for kidney failure differ by donor phenotype and estimate the population most at-risk for later kidney failure.
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images

Recently deep learning has been playing a major role in the field of computer vision. One of its applications is the reduction of human judgment in the diagnosis of diseases. Especially, brain tumor diagnosis requires high accuracy, where minute errors in judgment may lead to disaster. For this reason, brain tumor segmentation is an important challenge for medical purposes. Currently several methods exist for tumor segmentation but they all lack high accuracy. Here we present a solution for brain tumor segmenting by using deep learning. In this work, we studied different angles of brain MR images and applied different networks for segmentation. The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network. Experimental evaluations of the networks show that Dice score of 0.73 is achieved for a single network and 0.79 in obtained for multiple networks.
Passive stiffness of the hamstrings and the rectus femoris in persons after an ACL reconstruction

ABSTRACT Objectives: The aim of the article is to investigate the passive stiffness (PS) of rectus femoris and hamstring muscles working for the knee joint in persons after anterior cruciate ligament (ACL) reconstruction surgery. Methods: Thirty subjects (8 women and 22 men) aged 19–46 (x = 28.2 ± 7) participated in the investigation; an experimental group comprised 15 persons (4 women and 11 men) who underwent ACL reconstruction surgery. The other persons had no knee injury and described themselves as healthy. All the subjects underwent an investigation of the PS of the hamstrings and the rectus femoris on the injured as well as on the healthy leg. Results: The data shows vital difference in the PS of the hamstring muscles between the healthy and injured limb (p < 0.05), a significant difference in the PS of the rectus femoris (p < 0.001) was noted. Furthermore, the PS level of the hamstring muscles of the operated limb in the experimental group is significantly different to the both legs in control group (p < 0.001). Conclusions: Injury of the ACL results in a considerable increase in passive stiffness of the hamstring and rectus femoris muscles in both the injured leg and the healthy leg.
Survival of the Fastest: The Multivariate Optimization of Performance Phenotypes

Introduction Trade-offs are widespread in biological systems. Any investment in one trait must necessarily limit the investment in other traits. Still, many studies of physiological performance produce positive correlations between traits that are expected to trade-off with one another. Here we investigate why predicted trade-offs may often go unmeasured in studies of human athletes. Methods Triathletes compete in consecutive swimming, cycling, and running events as a single competition, events whose physical demands may be especially prone to generating performance trade-offs. Performance variation in these three events interacts to explain overall variation in athletic performance. Results We show that individual variation in athletic performance can mask trade-offs among disciplines, giving the impression that high-performance triathletes are athletic generalists. Covariance in race performance across the three disciplines was positive in the most elite athletes but became increasingly negative as race times increased. Conclusions These performance trade-offs among the disciplines preclude the realization of a generalist athlete except in the most elite triathletes, a result similar to the “big houses, big cars” phenomenon in life history evolution. This distinction between trait combinations that are favored for optimal performance versus constrained by trade-offs was only apparent when accounting for individual level variation in athletic performance. Our results provide further evidence that meaningful trade-offs may be missed if individual variation in quality is disregarded.
Effects of zooplankton carcasses degradation on freshwater bacterial community composition and implications for carbon cycling

Non-predatory mortality of zooplankton provides an abundant, yet, little studied source of high quality labile organic matter (LOM) in aquatic ecosystems. Using laboratory microcosms, we followed the decomposition of organic carbon of fresh 13C-labelled Daphnia carcasses by natural bacterioplankton. The experimental setup comprised blank microcosms i.e. artificial lake water without any organic matter additions (B), and microcosms either amended with natural humic matter (H), fresh Daphnia carcasses (D) or both, i.e. humic matter and Daphnia carcasses (HD). Most of the carcass carbon was consumed and respired by the bacterial community within 15 days of incubation. A shift in the bacterial community composition shaped by labile carcass carbon and by humic matter was observed. Nevertheless, we did not observe a quantitative change in humic matter degradation by heterotrophic bacteria in the presence of LOM derived from carcasses. However, carcasses were the main factor driving the bacterial community composition suggesting that the presence of large quantities of dead zooplankton might affect the carbon cycling in aquatic ecosystems. Our results imply that organic matter derived from zooplankton carcasses is efficiently remineralized by a highly specific bacterial community, but doesn’t interfere with the bacterial turnover of more refractory humic matter.
The MADS-box Transcription Factor PsMAD1 Is Involved in Zoosporogenesis and Pathogenesis of Phytophthora sojae

Transcriptional regulation is critical for plant pathogen development and virulence. MADS-box transcription factors belong to a highly conserved transcriptional regulator family in eukaryotic organisms that are involved in various important biological processes. Only one predicted MADS-box gene, PsMAD1, was identified in Phytophthora sojae, which was highly expressed during the sporangia and infection stages. To investigate its function, we generated PsMAD1 knockout mutants using the CRISPR/Cas9 system. Compared with the wild-type strain, the mutants showed no changes in vegetative growth, oospore production, or no differences in sensitivity to various abiotic stresses. Although sporangia production was normal, no zoospore release was detected in PsMAD1 mutants. Microscopy analyses revealed failure of cleavage of the cytoplasm into uninucleate zoospores in the mutants. In addition, the mutants showed reduced virulence in soybean. RNA-seq data indicated that PsMAD1 may regulate many zoospore development and infection associated genes. Thus, PsMAD1 may be a major regulator of P. sojae involved in zoosporogenesis and pathogenesis.
Complete Closure of Gastric Defect with Improved Purse-String Suture Technique Using Single-Channel Endoscope

To the Editor: Recently, with the development of endoscopic therapy technology, the incidence of iatrogenic perforation is also increasing. If the defect was not treated properly, subsequent gastrointestinal fistula and infection could lead to serious complications. This study evaluated a simple method for close gastric defect after endoscopic treatment, with endoloop and metallic clips using single‐channel endoscope.
Printing-while-moving: a new paradigm for large-scale robotic 3D Printing

Building and Construction have recently become an exciting application ground for robotics. In particular, rapid progress in material formulation and in robotics technology has made robotic 3D Printing of concrete a promising technique for in-situ construction. Yet, scalability remains an important hurdle to widespread adoption: the printing systems (gantry-based or arm-based) are often much larger than the structure to be printed, hence cumbersome. Recently, a mobile printing system–a manipulator mounted on a mobile base – was proposed to alleviate this issue: such a system, by moving its base, can potentially print a structure larger than itself. However, the proposed system could only print while being stationary, imposing thereby a limit on the size of structures that can be printed in a single take. Here, we develop a system that implements the printing-while-moving paradigm, which enables printing single-piece structures of arbitrary sizes with a single robot. This development requires solving motion planning, localization, and motion control problems that are specific to mobile 3D Printing. We report our framework to address those problems, and demonstrate, for the first time, a printing-while-moving experiment, wherein a 210 cm × 45 cm × 10 cm concrete structure is printed by a robot arm that has a reach of 87 cm.
A new method for protein structure reconstruction from NOESY distances

Protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments largely relies on computational algorithms. Recently, some effective low-rank matrix completion (MC) methods, such as ASD and ScaledASD, have been successfully applied to image processing, which inspires us to apply the methods to reconstruct protein structures. In this paper, we present an efficient method to determine protein structures based on experimental NMR NOESY distances. ScaledASD algorithm is used in the method with several post-procedures including chirality refinement, distance lower (upper) bound refinement, force field-based energy minimization (EM) and water refinement. By comparing several metrics in the conformation evaluation on our results with Protein Data Bank (PDB) structures, we conclude that our method is consistent with the popularly used methods. In particular, our results show higher validities in Procheck dihedral angles G-factor. Furthermore, we compare our calculation results with PDB structures by examining the structural similarity to X-ray crystallographic structures in a special dataset. The software and its MATLAB source codes are available in this https URL
A Hierarchical Silver‐Nanowire–Graphene Host Enabling Ultrahigh Rates and Superior Long‐Term Cycling of Lithium‐Metal Composite Anodes

Metallic lithium (Li) is a promising anode for next‐generation high‐energy‐density batteries, but its applications are still hampered due to the limited charging/discharging rate and poor cycling performance. Here, a hierarchical 3D porous architecture is designed with a binary network of continuous silver nanowires assembled on an interconnected 3D graphene skeleton as the host for Li‐metal composite anodes, which offers a significant boost in both charging/discharging rates and long‐term cycling performance for Li‐metal batteries. This unique hierarchical binary network structure in conjunction with optimized material combination provides ultrafast, continuous, and smooth electron transportation channel and non‐nucleation barrier sites to direct and confine Li deposition. It also offers outstanding mechanical strength and toughness to support massive Li deposition and buffer the internal stress fluctuations during long‐term repeated Li stripping/plating thereby minimizing fundamental issues of dendrite formation and volume change even under ultrafast charging/discharging rates. As a result, the composite anode using this hierarchical host can work smoothly at an unprecedented high current density of 40 mA cm‐2 over 1000 plating/stripping cycles with low overpotential (<120 mV) in symmetric cells. The as‐constructed full cell, paired with LiNi0.5Co0. 2Mn0.3O2 cathode, also exhibits excellent rate capability and high‐rate cycling stability.
Can Deep Clinical Models Handle Real-World Domain Shifts?

The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a game changer in building predictive models, thereby leading to community-wide data curation efforts. However, due to the inherent variabilities in population characteristics and biological systems, these models are often biased to the training datasets. This can be limiting when models are deployed in new environments, particularly when there are systematic domain shifts not known a priori. In this paper, we formalize these challenges by emulating a large class of domain shifts that can occur in clinical settings, and argue that evaluating the behavior of predictive models in light of those shifts is an effective way of quantifying the reliability of clinical models. More specifically, we develop an approach for building challenging scenarios, based on analysis of \textit{disease landscapes}, and utilize unsupervised domain adaptation to compensate for the domain shifts. Using the openly available MIMIC-III EHR dataset for phenotyping, we generate a large class of scenarios and evaluate the ability of deep clinical models in those cases. For the first time, our work sheds light into data regimes where deep clinical models can fail to generalize, due to significant changes in the disease landscapes between the source and target landscapes. This study emphasizes the need for sophisticated evaluation mechanisms driven by real-world domain shifts to build effective AI solutions for healthcare.