Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary zinc intake and mortality in patients with intestinal-type gastric cancer: A prospective cohort study in Korea

Purpose Current evidence regarding the association between zinc intake and gastric cancer (GC)-specific survival in patients with intestinal-type GC is lacking. Therefore, this cohort study investigated the association between zinc intake and GC mortality through follow-up on GC death among patients with intestinal-type GC and whether these effects differ according to the source of zinc intake. Methods A total of 185 patients with intestinal-type GC were enrolled from two hospitals between 2002 and 2006. Their survival or death was prospectively followed up until December 31, 2016, through a review of medical records and telephone surveys. Results A total of 178 patients were included and analyzed. The median follow-up period was 7.3 years. In the fully adjusted models, the highest tertile of total zinc intake showed a significantly lower GC mortality than the lowest tertile (hazard ratio, 0.22; 95% confidence interval: 0.08–0.64). In addition, the tertile of total zinc intake showed a dose-response association with GC mortality (p=0.015). Analysis of the source of zinc intake revealed that when zinc intake from staples (rice and noodles), animal, and plant food sources were combined, the results were similar to those of total zinc intake and GC mortality. Conclusion Zinc intake through various foods may be effective in reducing GC mortality by achieving balance with other nutrients. Our results suggest that zinc improves the survival of patients with intestinal-type GC in Korea.
Comprehensive functional and anatomic assessment of myocardial bridging: Unlocking the Gordian Knot

Myocardial bridging (MB) is the most frequent congenital coronary anomaly in which a segment of an epicardial coronary artery takes a tunneled course under a bridge of the myocardium. This segment is compressed during systole, resulting in the so-called “milking effect” at coronary angiography. As coronary blood flow occurs primarily during diastole, the clinical relevance of MB is heterogeneous, being usually considered an asymptomatic bystander. However, many studies have suggested its association with myocardial ischemia, anginal symptoms, and adverse cardiac events. The advent of contemporary non-invasive and invasive imaging modalities and the standardization of intracoronary functional assessment tools have remarkably improved our understanding of MB-related ischemia, suggesting the role of atherosclerotic lesions proximal to MB, vasomotor disorders and microvascular dysfunction as possible pathophysiological substrates. The aim of this review is to provide a contemporary overview of the pathophysiology and of the non-invasive and invasive assessment of MB, in the attempt to implement a case-by-case therapeutic approach according to the specific endotype of MB-related ischemia.
Applications of nanobodies in brain diseases

Nanobodies are antibody fragments derived from camelids, naturally endowed with properties like low molecular weight, high affinity and low immunogenicity, which contribute to their effective use as research tools, but also as diagnostic and therapeutic agents in a wide range of diseases, including brain diseases. Also, with the success of Caplacizumab, the first approved nanobody drug which was established as a first-in-class medication to treat acquired thrombotic thrombocytopenic purpura, nanobody-based therapy has received increasing attention. In the current review, we first briefly introduce the characterization and manufacturing of nanobodies. Then, we discuss the issue of crossing of the brain-blood-barrier (BBB) by nanobodies, making use of natural methods of BBB penetration, including passive diffusion, active efflux carriers (ATP-binding cassette transporters), carrier-mediated influx via solute carriers and transcytosis (including receptor-mediated transport, and adsorptive mediated transport) as well as various physical and chemical methods or even more complicated methods such as genetic methods via viral vectors to deliver nanobodies to the brain. Next, we give an extensive overview of research, diagnostic and therapeutic applications of nanobodies in brain-related diseases, with emphasis on Alzheimer’s disease, Parkinson’s disease, and brain tumors. Thanks to the advance of nanobody engineering and modification technologies, nanobodies can be linked to toxins or conjugated with radionuclides, photosensitizers and nanoparticles, according to different requirements. Finally, we provide several perspectives that may facilitate future studies and whereby the versatile nanobodies offer promising perspectives for advancing our knowledge about brain disorders, as well as hopefully yielding diagnostic and therapeutic solutions.
Yang-Mills mass gap, Floer homology, glueball spectrum, and conformal window in large-N QCD

Morse-Smale-Floer homology associates the critical points of the action functional of a classical field theory over a manifold to its homology. We associate to the intersection homology of certain Lagrangian submanifolds of R^4 the critical points of a quantum effective action of large-N SU(N) YM. For this purpose we construct in YM a trivial Topological Field Theory defined by twistor Wilson loops whose v.e. v. is 1 in the large-N limit for any shape of the loops supported on certain punctured Lagrangian submanifolds. We derive a new holomorphic loop equation for the twistor Wilson loops, that involves the change of variables in the YM functional integral from the connection to the anti-selfdual part of the curvature and the choice of a holomorphic gauge. Employing the holomorphic loop equation, and viewing Floer homology the other way around, we associate to arcs asymptotic in both directions to the cusps of the Lagrangian submanifolds the critical points of an effective action, that turn out to be surface operators of Z(N) holonomy. At the next-to-leading 1/N order a certain correlator of surface operators is non-topological and non-trivial, controls the mass gap of YM theory, and is saturated by an infinite sum of pure poles of scalar and pseudoscalar glueballs with positive charge conjugation. It satisfies asymptotically for large momentum fundamental universal constraints arising from the asymptotic freedom and the renormalization group. We predict at large-N the ratio of the masses of the two lower-mass scalar glueballs r=\sqrt 2=1.414, to be compared with the measure in lattice SU(8) YM by Meyer-Teper r=1.42(11), and with the value implied by PDG(2014) r=1.397(008). The construction extends to massless Veneziano large-N limit of QCD, for which we determine the lower edge of the conformal window N_f/N=5/2 and the corresponding quark-mass anomalous dimension gamma=-4/5.
Integral points on the complement of plane quartics

Let $Y$ be the complement of a plane quartic curve $D$ defined over a number field. Our main theorem confirms the Lang-Vojta conjecture for $Y$ when $D$ is a generic smooth quartic curve, by showing that its integral points are confined in a curve except for a finite number of exceptions. The required finiteness will be obtained by reducing it to the Shafarevich conjecture for K3 surfaces. Some variants of our method confirm the same conjecture when $D$ is a reducible generic quartic curve which consists of four lines, two lines and a conic, or two conics.
Dimension invariants for groups admitting a cocompact model for proper actions

Let $G$ be a group that admits a cocompact classifying space for proper actions $X$. We derive a formula for the Bredon cohomological dimension for proper actions of $G$ in terms of the relative cohomology with compact support of certain pairs of subcomplexes of $X$. We use this formula to compute the Bredon cohomological dimension for proper actions of fundamental groups of non-positively curved simple complexes of finite groups. As an application we show that if a virtually torsion-free group acts properly and chamber transitively on a building, its virtual cohomological dimension coincides with its Bredon cohomological dimension. This covers the case of Coxeter groups and graph products of finite groups.
Multifactor dimensionality reduction reveals the effect of interaction between ERAP1 and IFIH1 polymorphisms in psoriasis susceptibility genes

Background: Psoriasis is a common immune-mediated hyperproliferative skin dysfunction with known genetic predisposition. Gene–gene interaction (e.g., between HLA-C and ERAP1) in the psoriasis context has been reported in various populations. As ERAP1 has been recognized as a psoriasis susceptibility gene and plays a critical role in antigen presentation, we performed this study to identify interactions between ERAP1 and other psoriasis susceptibility gene variants. Methods: We validated psoriasis susceptibility gene variants in an independent cohort of 5,414 patients with psoriasis and 5,556 controls. Multifactor dimensionality reduction (MDR) analysis was performed to identify the interaction between variants significantly associated with psoriasis in the validation cohort and ERAP1 variants. We then conducted a meta-analysis of those variants with datasets from exome sequencing, target sequencing, and validation analyses and used MDR to identify the best gene–gene interaction model, including variants that were significant in the meta-analysis and ERAP1 variants. Results: We found that 19 of the replicated variants were identified with p < 0.05 and detected six single-nucleotide polymorphisms of psoriasis susceptibility genes in the meta-analysis. MDR analysis revealed that the best predictive model was that between the rs27044 polymorphism of ERAP1 and the rs7590692 polymorphism of IFIH1 (cross-validation consistency = 9/10, test accuracy = 0.53, odds ratio = 1.32 (95% CI, 1.09–1.59), p < 0.01). Conclusion: Our findings suggest that the interaction between ERAP1 and IFIH1 affects the development of psoriasis. This hypothesis needs to be tested in basic biological studies.
Relationship between maternal–infant gut microbiota and infant food allergy

The gut microbiota plays a crucial role in food allergies. We sought to identify characteristics of the maternal gut microbiota in the third trimester and the infant gut microbiota in early life and the association of these microbiotas with infant food allergy. A total of 68 healthy pregnant women and their full-term newborns were selected from a cohort of 202 mother–infant pairs; among them, 24 infants had been diagnosed with food allergy within 1 year of age, whereas 44 infants were healthy without allergic symptoms. We collected 65 maternal fecal samples before delivery and 253 infant fecal samples at five time points following birth. Fecal samples were microbiologically analyzed using 16S rRNA gene sequencing. Holdemania abundance in the maternal gut microbiota in the third trimester was significantly higher in the non-allergy group than in the food allergy group (P = 0.036). In the infant gut microbiota, Holdemania was only found in meconium samples; its abundance did not differ significantly between the two groups. The change in the abundance of Actinobacteria over time differed between the non-allergy and food allergy groups (FA, P = 0.013; NA, P = 9.8 × 10−5), and the change in the abundance of Firmicutes over time differed significantly in the non-allergy group (P = 0.023). The abundances of genera Anaerotruncus, Roseburia, Ruminococcus, and Erysipelotricaceae were significantly different between the non-allergy and food allergy groups at different time points. Our results showed that maternal carriage of Holdemania during the third trimester strongly predicted the absence of food allergies in infants; there was no correlation between the presence of food allergies and the abundance of Holdemania in the infant gut microbiota. More dynamic fluctuations in phyla Actinobacteria and Firmicutes early in life protect against food allergy. Thus, the enrichment of the infant gut microbiota early in life with short-chain fatty acid-producing bacteria may be beneficial in preventing the development of food allergies in infants.
Challenges and advances in measuring sap flow in agriculture and agroforestry: A review with focus on nuclear magnetic resonance

Sap flow measurement is one of the most effective methods for quantifying plant water use.A better understanding of sap flow dynamics can aid in more efficient water and crop management, particularly under unpredictable rainfall patterns and water scarcity resulting from climate change. In addition to detecting infected plants, sap flow measurement helps select plant species that could better cope with hotter and drier conditions. There exist multiple methods to measure sap flow including heat balance, dyes and radiolabeled tracers. Heat sensor-based techniques are the most popular and commercially available to study plant hydraulics, even though most of them are invasive and associated with multiple kinds of errors. Heat-based methods are prone to errors due to misalignment of probes and wounding, despite all the advances in this technology. Among existing methods for measuring sap flow, nuclear magnetic resonance (NMR) is an appropriate non-invasive approach. However, there are challenges associated with applications of NMR to measure sap flow in trees or field crops, such as producing homogeneous magnetic field, bulkiness and poor portable nature of the instruments, and operational complexity. Nonetheless, various advances have been recently made that allow the manufacture of portable NMR tools for measuring sap flow in plants. The basic concept of the portal NMR tool is based on an external magnetic field to measure the sap flow and hence advances in magnet types and magnet arrangements (e.g., C-type, U-type, and Halbach magnets) are critical components of NMR-based sap flow measuring tools. Developing a non-invasive, portable and inexpensive NMR tool that can be easily used under field conditions would significantly improve our ability to monitor vegetation responses to environmental change.
Some differences but all at risk: Improving farm safety for young people—An Australian experience

A significant portion of on-farm deaths and injuries in Australia occur among young people working on the farm. Since most Australian farms are still family owned and operated, young people are an integral part of everyday operations and the farm is a place where these young people live, work and play. This paper describes how the international Gear Up for Ag Health and Safety™ program, originally developed in North America, was further developed for a younger Australian audience (ages 12–19) enrolled in agricultural programs at secondary or vocational schools. In addition, we share insight on demographics, self-reported farm safety behaviours, and the most common farm tasks being performed by program participants utilising a pre-survey originally developed for program customisation. Of particular importance were the most common farming tasks reported by this group. The most common tasks performed on Australian farms included a large variety of vehicle use (farm vehicles, motorbikes, and quadbikes) and handling livestock. Females reported operating vehicles and other farm equipment at the same rates as males. Males were more likely to be working with large heavy machinery and driving trucks, while females were more likely to be working with livestock and using horses for stockwork. Both males and females reported low use of PPE and poor safety habits. In future Australian programs, it will be important to address the conspicuous use of motor vehicles, quadbikes, motor bikes and machinery at early ages, and to target gender-specific tasks to reduce risks on the farm.
Gall-specific promoter, an alternative to the constitutive CaMV35S promoter, drives host-derived RNA interference targeting Mi-msp2 gene to confer effective nematode resistance

One of the major obligate plant parasites causing massive economic crop losses belongs to the class of root-knot nematodes (RKNs). Targeting of major nematode parasitism genes via Host Delivered-RNAi (HD-RNAi) to confer silencing is established as one of the most effective approaches to curb nematode infection. Utilizing nematode-responsive root-specific (NRRS) promoters to design a dsRNA molecule targeting approach to hamper nematode parasitism. Here, a previously validated peroxidase gall specific promoter, pAt2g18140, from Arabidopsis was employed to express the dsRNA construct of the nematode effector gene Mi-msp2 from Meloidogyne incognita. Arabidopsis RNAi lines of CaMV35S::Mi-msp2-RNAi and pAt2g18140::Mi-msp2-RNAi were compared with control plants to assess the decrease in plant nematode infection. When subjected to infection, the maximum reductions in the numbers of galls, females and egg masses in the CaMV35S::Mi-msp2-RNAi lines were 61%, 66% and 95%, respectively, whereas for the pAt2g18140::Mi-msp2-RNAi lines, they were 63%, 68% and 100%, respectively. The reduction in transcript level ranged from 79%-82% for CaMV35S::Mi-msp2-RNAi and 72%-79% for the pAt2g18140::Mi-msp2-RNAi lines. Additionally, a reduction in female size and a subsequent reduction in next-generation fecundity demonstrate the efficacy and potential of the gall specific promoter pAt2g18140 for utilization in the development of HD-RNAi constructs against RKN, as an excellent alternative to the CaMV35S promoter.
Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model

Accurate and rapid identification of the effective number of panicles per unit area is crucial for the assessment of rice yield. As part of agricultural development, manual observation of effective panicles in the paddy field is being replaced by unmanned aerial vehicle (UAV) imaging combined with target detection modeling. However, UAV images of panicles of curved hybrid Indica rice in complex field environments are characterized by overlapping, blocking, and dense distribution, imposing challenges on rice panicle detection models. This paper proposes a universal curved panicle detection method by combining UAV images of different types of hybrid Indica rice panicles (leaf-above-spike, spike-above-leaf, and middle type) from four ecological sites using an improved You Only Look Once version 4 (YOLOv4) model. MobileNetv2 is used as the backbone feature extraction network based on a lightweight model in addition to a focal loss and convolutional block attention module for improved detection of curved rice panicles of different varieties. Moreover, soft non-maximum suppression is used to address rice panicle occlusion in the dataset. This model yields a single image detection rate of 44.46 FPS, and mean average precision, recall, and F1 values of 90.32%, 82.36%, and 0.89%, respectively. This represents an increase of 6.2%, 0.12%, and 16.24% from those of the original YOLOv4 model, respectively. The model exhibits superior performance in identifying different strain types in mixed and independent datasets, indicating its feasibility as a general model for detection of different types of rice panicles in the heading stage.
An Iterative Regularized Incremental Projected Subgradient Method for a Class of Bilevel Optimization Problems

We study a class of bilevel convex optimization problems where the goal is to find the minimizer of an objective function in the upper level, among the set of all optimal solutions of an optimization problem in the lower level. A wide range of problems in convex optimization can be formulated using this class. An important example is the case where an optimization problem is ill-posed. In this paper, our interest lies in addressing the bilevel problems, where the lower level objective is given as a finite sum of separate nondifferentiable convex component functions. This is the case in a variety of applications in distributed optimization, such as large-scale data processing in machine learning and neural networks. To the best of our knowledge, this class of bilevel problems, with a finite sum in the lower level, has not been addressed before. Motivated by this gap, we develop an iterative regularized incremental subgradient method, where the agents update their iterates in a cyclic manner using a regularized subgradient. Under a suitable choice of the regularization parameter sequence, we establish the convergence of the proposed algorithm and derive a rate of $\mathscr{O}(1/k^{0.5-\varepsilon})$ in terms of the lower level objective function for an arbitrary small $\varepsilon > 0$. We present the performance of the algorithm on a binary text classification problem.
Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury

Artificial intelligence (AI) has played a crucial role in advancing biomedical sciences but has yet to have the impact it merits in regulatory science. As the field advances, in silico and in vitro approaches have been evaluated as alternatives to animal studies, in a drive to identify and mitigate safety concerns earlier in the drug development process. Although many AI tools are available, their acceptance in regulatory decision-making for drug efficacy and safety evaluation is still a challenge. It is a common perception that an AI model improves with more data, but does reality reflect this perception in drug safety assessments? Importantly, a model aiming at regulatory application needs to take a broad range of model characteristics into consideration. Among them is adaptability, defined as the adaptive behavior of a model as it is retrained on unseen data. This is an important model characteristic which should be considered in regulatory applications. In this study, we set up a comprehensive study to assess adaptability in AI by mimicking the real-world scenario of the annual addition of new drugs to the market, using a model we previously developed known as DeepDILI for predicting drug-induced liver injury (DILI) with a novel Deep Learning method. We found that the target test set plays a major role in assessing the adaptive behavior of our model. Our findings also indicated that adding more drugs to the training set does not significantly affect the predictive performance of our adaptive model. We concluded that the proposed adaptability assessment framework has utility in the evaluation of the performance of a model over time.