Effect of the presence of virus-like particles on bacterial growth in sunlit xurface and dark deep ocean environments in the southern East China Sea

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tsai, A.-Y., Lin, Y.-T., & Gong, G.-C. Effect of the presence of virus-like particles on bacterial growth in sunlit xurface and dark deep ocean environments in the southern East China Sea. Water, 13(20), (2021): 2934, https://doi.org/10.3390/w13202934.Virus-like particles (VLPs) are thought to increase the dissolved organic carbon by releasing the contents of the host cell, which, in turn, can affect bacterial growth in natural aquatic environments. Yet, experimental tests have shown that the effect of VLPs on the bacterial growth rate at different depths has seldom been studied. Bacteria–VLP interaction and the effect of VLPs on bacterial growth rate in the sunlit surface (3 m) and dark, deep ocean (130 m) environments were first explored at a test site in the southern East China Sea of the northwest Pacific. Our experimental results indicated that bacterial and virus-like particle (VLP) abundance decreased with depth from 0.8 ± 0.3 × 105 cells mL−1 and 1.8 ± 0.4 × 106 VLPs mL−1 at 3 m to 0.4 ± 0.1 × 105 cells mL−1 and 1.4 ± 0.3 × 106 VLPs mL−1 at 130 m. We found that the abundance of VLPs to Bacteria Ratio (VBR) in the dark deep ocean (VBR = 35.0 ± 5.6) was higher than in the sunlit surface environment (VBR = 22.5 ± 2.1). The most interesting finding is that in the dark, deep ocean region the bacterial growth rate in the presence of VLPs was higher (0.05 h−1) than that in virus-diluted treatments (0.01 h−1). However, there was no significant difference in the bacterial growth rates between the treatments in the sunlit surface ocean region. Deep-sea ecosystems are dark and extreme environments that lack primary photosynthetic production, and our estimates imply that the contribution of recycled carbon by viral lysis is highly significant for bacterial growth in the dark, deep ocean environment. Further work for more study sites is needed to identify the relationship of VLPs and their hosts to enable us to understand the role of VLPs at different depths in the East China Sea.The research was supported by RFBR projects 18-44-920026, and the Ministry of Science and Technology, Taiwan, grant number NSC 109-2611-M-019-013 and NSC 110-2611-M-019-005

Critical Neurons: optimized visual recognition in a neuronal network

In the past few decades, there have been intense debates whether the brain operates at a critical state. To verify the criticality hypothesis in the neuronal networks is challenging and the accumulating experimental and theoretical results remain controversial at this point. Here we simulate how visual information of a nature image is processed by the finite Kinouchi-Copelli neuronal network, extracting the trends of the mutual information (how sensible the neuronal network is), the dynamical range (how sensitive the network responds to external stimuli) and the statistical fluctuations (how criticality is defined in conventional statistical physics). It is rather remarkable that the optimized state for visual recognition, although close to, does not coincide with the critical state where the statistical fluctuations reach the maximum. Different images and/or network sizes of course lead to differences in details but the trend of the information optimization remains the same. Our findings pave the first step to investigate how the information processing is optimized in different neuronal networks and suggest that the criticality hypothesis may not be necessary to explain why a neuronal network can process information smartly.Comment: 11 pages, 4 figures in the main text and 3 figures in the Supplementary Informatio

Lactobacillus plantarum MYL26 induces endotoxin tolerance phenotype in Caco-2 cells

Background: Crohn's disease and ulcerative colitis are the major types of chronic inflammatory bowel diseaseoccurring in the colon and small intestine. A growing body of research has proposed that probiotics are able toattenuate the inflammatory symptoms of these diseases in vitro and in vivo. However, the mechanism of probioticactions remains unclear.Results: Our results suggested Lactobacillus plantarum MYL26 inhibited inflammation in Caco-2 cells throughregulation of gene expressions of TOLLIP, SOCS1, SOCS3, and IκBα, rather than SHIP-1 and IRAK-3.Conclusions: We proposed that live/ heat-killed Lactobacillus plantarum MYL26 and bacterial cell wall extracttreatments impaired TLR4-NFκb signal transduction through Tollip, SOCS-1 and SOCS-3 activation, thus inducing LPStolerance. Our findings suggest that either heat-killed probiotics or probiotic cell wall extracts are able to attenuateinflammation through pathways similar to that of live bacteria

The effects of rear-wheel camber on the kinematics of upper extremity during wheelchair propulsion

BACKGROUND: The rear-wheel camber, defined as the inclination of the rear wheels, is usually used in wheelchair sports, but it is becoming increasingly employed in daily propulsion. Although the rear-wheel camber can increase stability, it alters physiological performance during propulsion. The purpose of the study is to investigate the effects of rear-wheel cambers on temporal-spatial parameters, joint angles, and propulsion patterns. METHODS: Twelve inexperienced subjects (22.3±1.6 yr) participated in the study. None had musculoskeletal disorders in their upper extremities. An eight-camera motion capture system was used to collect the three-dimensional trajectory data of markers attached to the wheelchair-user system during propulsion. All participants propelled the same wheelchair, which had an instrumented wheel with cambers of 0°, 9°, and 15°, respectively, at an average velocity of 1 m/s. RESULTS: The results show that the rear-wheel camber significantly affects the average acceleration, maximum end angle, trunk movement, elbow joint movement, wrist joint movement, and propulsion pattern. The effects are especially significant between 0° and 15°. For a 15° camber, the average acceleration and joint peak angles significantly increased (p < 0.01). A single loop pattern (SLOP) was adopted by most of the subjects. CONCLUSIONS: The rear-wheel camber affects propulsion patterns and joint range of motion. When choosing a wheelchair with camber adjustment, the increase of joint movements and the base of support should be taken into consideration

Ring Chromosome 7 Presenting with Intrauterine Growth Restriction and Multiple Anomalies

SummaryObjectiveRing chromosome 7 is a very rare chromosomal anomaly that may have a grave prognosis. Nevertheless, the clinical features associated with ring chromosome 7 are highly variable. Here, we report a case with ring chromosome 7 and the perinatal findings.Case ReportA 32-year-old, gravida 1, para 0, woman was referred to our hospital because of intrauterine growth restriction (IUGR) and oligohydramnios at 35 weeks of gestation. Prenatal ultrasound revealed a severe IUGR fetus presenting with multicystic kidney, hydronephrosis and oligohydramnios. At parturition, the birth weight of this male infant was 1,720 g, and a battery of anomalies were also noted, including imperforate anus, hypospadia, micropenis, right cryptorchidism, severe IUGR, multiple nevi on the forehead, shoulder and left thigh, brain atrophy, right multicystic kidney, and left mild hydronephrosis. Cytogenetic study from cord blood revealed a ring chromosome 7.ConclusionRing chromosome 7 is extremely rare and our case might be the 15th and youngest case in the medical literature. Our case had multicystic kidney and imperforate anus, which have not been reported previously. Prenatal diagnosis of ring chromosome 7 is very difficult. When fetuses present with severe IUGR, oligohydramnios and multicystic kidney, chromosomal aberrations should be kept in mind, and perinatal cytogenetic workup is warranted

Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning

Stroke is a common disabling neurological condition that affects about one-quarter of the adult population over age 25; more than half of patients still have poor outcomes, such as permanent functional dependence or even death, after the onset of acute stroke. The aim of this study is to investigate the efficacy of diffusion-weighted MRI modalities combining with structured health profile on predicting the functional outcome to facilitate early intervention. A deep fusion learning network is proposed with two-stage training: the first stage focuses on cross-modality representation learning and the second stage on classification. Supervised contrastive learning is exploited to learn discriminative features that separate the two classes of patients from embeddings of individual modalities and from the fused multimodal embedding. The network takes as the input DWI and ADC images, and structured health profile data. The outcome is the prediction of the patient needing long-term care at 3 months after the onset of stroke. Trained and evaluated with a dataset of 3297 patients, our proposed fusion model achieves 0.87, 0.80 and 80.45% for AUC, F1-score and accuracy, respectively, outperforming existing models that consolidate both imaging and structured data in the medical domain. If trained with comprehensive clinical variables, including NIHSS and comorbidities, the gain from images on making accurate prediction is not considered substantial, but significant. However, diffusion-weighted MRI can replace NIHSS to achieve comparable level of accuracy combining with other readily available clinical variables for better generalization.Comment: 12 pages, 5 figures, 5 table

Towards Optimizing with Large Language Models

In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes. Each of these tasks corresponds to unique optimization domains, and LLMs are required to execute these tasks with interactive prompting. That is, in each optimization step, the LLM generates new solutions from the past generated solutions with their values, and then the new solutions are evaluated and considered in the next optimization step. Additionally, we introduce three distinct metrics for a comprehensive assessment of task performance from various perspectives. These metrics offer the advantage of being applicable for evaluating LLM performance across a broad spectrum of optimization tasks and are less sensitive to variations in test samples. By applying these metrics, we observe that LLMs exhibit strong optimization capabilities when dealing with small-sized samples. However, their performance is significantly influenced by factors like data size and values, underscoring the importance of further research in the domain of optimization tasks for LLMs