High energy-charged cell factory for heterologous protein synthesis

Overexpression of gluconeogenic phosphoenolpyruvate carboxykinase (PCK) under glycolytic conditions enables Escherichia coli to maintain a greater intracellular ATP concentration and, consequently, to up-regulate genes for amino acid and nucleotide biosynthesis. To investigate the effect of a high intracellular ATP concentration on heterologous protein synthesis, we studied the expression of a foreign gene product, enhanced green fluorescence protein (eGFP), under control of the T7 promoter in E. coli BL21(DE3) strain overexpressing PCK. This strain was able to maintain twice as much intracellular ATP and to express two times more foreign protein than the control strain. These results indicate that a high energy-charged cell can be beneficial as a protein-synthesizing cell factory. The potential uses of such a cell factory are discussed

Singlet Fermionic Dark Matter with Dark ZZ

We present a fermionic dark matter model mediated by the hidden gauge boson. We assume the QED-like hidden sector which consists of a Dirac fermion and U(1)$_X$ gauge symmetry, and introduce an additional scalar electroweak doublet field with the U(1)$_X$ charge as a mediator. The hidden U(1)$_X$ symmetry is spontaneously broken by the electroweak symmetry breaking and there exists a massive extra neutral gauge boson in this model which is the mediator between the hidden and visible sectors. Due to the U(1)$_X$ charge, the additional scalar doublet does not couple to the Standard Model fermions, which leads to the Higgs sector of type I two Higgs doublet model. The new gauge boson couples to the Standard Model fermions with couplings proportional to those of the ordinary $Z$ boson but very suppressed, thus we call it the dark $Z$ boson. We study the phenomenology of the dark $Z$ boson and the Higgs sector, and show the hidden fermion can be the dark matter candidate.Comment: 10 pages, 3 figure

Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning

Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world scenarios, the number of input data and tasks is constantly changing in a statistical way, not a static way. Although recently introduced incremental learning scenarios having blurry task boundaries somewhat address the above issues, they still do not fully reflect the statistical properties of real-world situations because of the fixed ratio of disjoint and blurry samples. In this paper, we propose a new Stochastic incremental Blurry task boundary scenario, called Si-Blurry, which reflects the stochastic properties of the real-world. We find that there are two major challenges in the Si-Blurry scenario: (1) inter- and intra-task forgettings and (2) class imbalance problem. To alleviate them, we introduce Mask and Visual Prompt tuning (MVP). In MVP, to address the inter- and intra-task forgetting issues, we propose a novel instance-wise logit masking and contrastive visual prompt tuning loss. Both of them help our model discern the classes to be learned in the current batch. It results in consolidating the previous knowledge. In addition, to alleviate the class imbalance problem, we introduce a new gradient similarity-based focal loss and adaptive feature scaling to ease overfitting to the major classes and underfitting to the minor classes. Extensive experiments show that our proposed MVP significantly outperforms the existing state-of-the-art methods in our challenging Si-Blurry scenario

Biological Effect of Gas Plasma Treatment on CO 2

Porous polycaprolactone (PCL) scaffolds were fabricated by using the CO2 gas foaming/salt leaching process and then PCL scaffolds surface was treated by oxygen or nitrogen gas plasma in order to enhance the cell adhesion, spreading, and proliferation. The PCL and NaCl were mixed in the ratios of 3 : 1. The supercritical CO2 gas foaming process was carried out by solubilizing CO2 within samples at 50°C and 8 MPa for 6 hr and depressurization rate was 0.4 MPa/s. The oxygen or nitrogen plasma treated porous PCL scaffolds were prepared at discharge power 100 W and 10 mTorr for 60 s. The mean pore size of porous PCL scaffolds showed 427.89 μm. The gas plasma treated porous PCL scaffolds surface showed hydrophilic property and the enhanced adhesion and proliferation of MC3T3-E1 cells comparing to untreated porous PCL scaffolds. The PCL scaffolds produced from the gas foaming/salt leaching and plasma surface treatment are suitable for potential applications in bone tissue engineering

Deep learning-based phenotype classification of three ark shells: Anadara kagoshimensis, Tegillarca granosa, and Anadara broughtonii

The rapid and accurate classification of aquatic products is crucial for ensuring food safety, production efficiency, and economic benefits. However, traditional manual methods for classifying ark shell species based on phenotype are time-consuming and inefficient, especially during peak seasons when the demand is high and labor is scarce. This study aimed to develop a deep learning model for the automated identification and classification of commercially important three ark shells (Tegillarca granosa, Anadara broughtonii, and Anadara kagoshimensis) from images. The ark shells were collected and identified using a polymerase chain reaction method developed in a previous study, and a total of 1,400 images were categorized into three species. Three convolutional neural network (CNN) models, Visual Geometry Group Network (VGGnet), Inception-Residual Network (ResNet), and SqueezeNet, were then applied to two different classification sets, Set-1 (four bivalve species) and Set-2 (three ark shell species). Our results showed that SqueezeNet demonstrated the highest accuracy during the training phase for both classification sets, whereas Inception-ResNet exhibited superior accuracy during the validation phase. Similar results were obtained after developing a third classification set (Set-3) to classify six categories by combining Set-1 and Set-2. Overall, the developed CNN-based classification model exhibited a performance comparable or superior to that presented in previous studies and can provide a theoretical basis for bivalve classification, thereby contributing to improved food safety, production efficiency, and economic benefits in the aquatic products industry

A case of dermatomyositis in a patient with central core disease: unusual association with autoimmunity and genetic muscle disease

Background Dermatomyositis is an inflammatory muscle disease caused by immune-mediated muscle injury, and central core disease (CCD) is a congenital myopathy associated with disturbed intracellular calcium homeostasis and excitation-contraction coupling. To date, CCD has not been reported to have autoantibodies or coexist with inflammatory myopathy. Case presentation Here, we described the case of a 25-year-old woman who had progressive proximal muscle weakness, myalgia, pruritic macular rash, skin ulcers, and calcinosis. Dermatomyositis was initially suspected based on the clinical symptoms accompanied by elevated muscle enzyme levels, electromyography abnormalities, and a positive antinuclear antibody test. However, the patients muscle biopsy revealed the characteristic findings of both dermatomyositis and CCD, suggesting that dermatomyositis occurred in this patient with previously asymptomatic CCD. The patient did not have any pathogenic gene mutations associated with congenital myopathy, including RYR1 and SEPN1 in targeted next-generation sequencing. She received high-dose glucocorticoid therapy and azathioprine with a significant improvement in muscle strength. Conclusions We present a case of rare coexistence of dermatomyositis and CCD. Clinicians should be aware that patients with CCD may have inflammatory myopathy that responds well to immunosuppressive therapy.This study was funded by a grant from the Ministry of Science, ICT, and Future Planning (NRF-2020M3E5E2037430 to Y-W.S.)

Hennekam Syndrome: A Case Report

Hennekam syndrome is a rare autosomal recessive disorder resulting from malformation of the lymphatic system. The characteristic signs of Hennekam syndrome are lymphangiectasia, lymph edema, facial anomalies, and mental retardation. This is a case in which a patient presented with left-arm lymphedema, facial-feature anomalies, and multiple organ lymphangiectasia consistent with symptoms of Hennekam syndrome. There is no curative therapy at this time, but rehabilitative treatments including complete decongestive therapy for edema control appeared to be beneficial

Association of retroperitoneal fibrosis with malignancy and its outcomes

Introduction Retroperitoneal fibrosis (RPF) is characterized by a highly fibrotic retroperitoneal mass and encompasses the idiopathic form and secondary to malignancies. Because we have limited knowledge whether RPF is associated with malignancy, we aimed to investigate the relationship between RPF and malignancy and to compare the characteristics and prognosis of cancers among patients with RPF. Methods Medical records of 111 patients diagnosed as having RPF were reviewed and 38 cases of cancer, confirmed by biopsy, were identified. Standardized incidence ratios (SIRs) were calculated for cancers and stratified according to cancer type and RPF-cancer diagnosis interval. Cancer characteristics and outcomes were compared between RPF-cancer diagnosis intervals. Results The average age at RPF diagnosis was 59.2 ± 15.0 years, and 69.4% of the patients were male. The cancer SIRs in patients with RPF relative to age- and sex-matched individuals in the general population was 2.2 (1.6–3.1). SIRs of renal pelvis cancer and multiple myeloma were significantly higher than in the general population. When stratified by RPF-cancer intervals, the SIR for cancer was 9.9 within 1 year of RPF diagnosis, while no significant increase in the SIR was found after 1 year from RPF diagnosis. Cancer stage was more advanced at the time of diagnosis in patients within a 1-year interval for RPF than those with cancer within a >5-year interval, with a correspondingly increased mortality in the former patients. Conclusions RPF was significantly associated with malignancy, particularly those diagnosed within 1 year of RPF diagnosis. Cancer stages at diagnosis were more advanced and the mortality rate was higher in patients within a 1-year interval between RPF and cancer diagnosis than in those with a >5-year interval between diagnoses.This work was supported by Biomedical Research Institute grant, Kyungpook National University Hospital (2017), and by the research fund of Rheumatology Research Foundation (RRF-2016-05). This work was partly supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HI14C1277)