Insights into lignocellulose degradation: comparative genomics of anaerobic and cellulolytic Ruminiclostridium-type species

Mesophilic, anaerobic, and cellulolytic Ruminiclostridium-type bacterial species can secrete an extracellular, multi-enzyme machinery cellulosome, which efficiently degrades cellulose. In this study, we first reported the complete genome of Ruminiclostridium papyrosolvens DSM2782, a single circular 5,027,861-bp chromosome with 37.1% G + C content, and compared it with other Ruminiclostridium-type species. Pan-genome analysis showed that Ruminiclostridium-type species share a large number of core genes to conserve basic functions, although they have a high level of intraspecific genetic diversity. Especially, KEGG mapping revealed that Ruminiclostridium-type species mainly use ABC transporters regulated by two-component systems (TCSs) to absorb extracellular sugars but not phosphotransferase systems (PTSs) that are employed by solventogenic clostridia, such as Clostridium acetobutylicum. Furthermore, we performed comparative analyses of the species-specific repertoire of CAZymes for each of the Ruminiclostridium-type species. The high similarity of their cohesins suggests a common ancestor and potential cross-species recognition. Additionally, both differences between the C-terminal cohesins and other cohesins of scaffoldins and between the dockerins linking with cellulases and other catalytic domains indicate a preference for the location of cellulosomal catalytic subunits at scaffoldins. The information gained in this study may be utilized directly or developed further by genetic engineering and optimizing enzyme systems or cell factories for enhanced biotechnological biomass deconstruction and biofuel production

The history of degenerate (bipartite) extremal graph problems

This paper is a survey on Extremal Graph Theory, primarily focusing on the case when one of the excluded graphs is bipartite. On one hand we give an introduction to this field and also describe many important results, methods, problems, and constructions.Comment: 97 pages, 11 figures, many problems. This is the preliminary version of our survey presented in Erdos 100. In this version 2 only a citation was complete

Chemotherapy-induced nausea and vomiting among cancer patients in Shanghai: a cross-sectional study

Background and purpose: Chemotherapy-induced nausea and vomiting (CINV) can cause severe damage to body functions and even lead to death. The prevention of CINV is critically important in patients receiving emetogenic chemotherapy regimen. This study aimed to investigate the prevalence and treatment of CINV in Grade-A tertiary hospitals in Shanghai and explore risk factors of CINV to improve its management. Methods: The clinical data of 376 cancer patients in Grade-A tertiary hospitals in Shanghai from October 2022 to December 2022 were collected retrospectively. The questionnaire was used to conduct a cross-sectional study. The univariate and multivariable logistic regression models were used to evaluate the influencing factors of CINV. Results: The management and coincidence of the guideline in 2022 significantly improved compared to five years ago. For patients receiving high-emetic-risk chemotherapy regimen, the coincidence of the guideline increased from 21.6% to 67.0%. For patients receiving moderate-emetic-risk chemotherapy regimen, the neurokinin-1 (NK-1) receptor antagonist was not significantly associated with CINV. Multivariable analysis showed that the chemotherapy regimen was the only risk factor for CINV during the whole period (P<0.05). Conclusion: The chemotherapy regimen is the main risk factor for CINV. To control CINV better, clinical practitioners should focus on the intrinsic risk of chemotherapy regimens preferentially, estimate the risk and adhere better to guidelines

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Hybrid Approach Combining Modified Gravity Model and Deep Learning for Short-Term Forecasting of Metro Transit Passenger Flows

Short-term forecasting of metro transit passenger flows is of great importance to the urban subway system in the various aspects of train and crew scheduling, congestion mitigation strategies, operational decision-making, and dynamic information provision. In this paper, a hybrid short-term forecasting approach is developed by combining the modified gravity model and deep learning models (e.g., convolutional neural networks [CNN] with auto-encoder). There are three components in this hybrid forecasting approach: (a) the modified gravity model that incorporates both the geographic information surrounding metro stations and station-level inflows/outflows as regression attributes; (b) the convolutional auto-encoder that tackles the sparsity issues of origin–destination (OD) matrices of passenger flows; and (c) the fusion of physical regression results and the decoder matrix, where the backpropagation algorithm is applied to tune the optimal fusion weight parameter matrix. The combination enables the proposed approach to achieve the trade-off between model interpretability and forecasting accuracy. The proposed approach is applied to the short-term forecasting of passenger flows for the metro transit network in Beijing, China. The experimental results show that the hybrid approach is promising and outperforms the benchmark models, for example, time-series models, long short-term memory, and CNN. The application demonstrates that the proposed hybrid short-term forecasting approach is suitable in both the station-level trip generation/attraction and the inter-station OD passenger flows

Recent Progress in Silicon-Based Slow-Light Electro-Optic Modulators

As an important optoelectronic integration platform, silicon photonics has achieved significant progress in recent years, demonstrating the advantages on low power consumption, low cost, and complementary metal–oxide–semiconductor (CMOS) compatibility. Among the different silicon photonics devices, the silicon electro-optic modulator is a key active component to implement the conversion of electric signal to optical signal. However, conventional silicon Mach–Zehnder modulators and silicon micro-ring modulators both have their own limitations, which will limit their use in future systems. For example, the conventional silicon Mach–Zehnder modulators are hindered by large footprint, while the silicon micro-ring modulators have narrow optical bandwidth and high temperature sensitivity. Therefore, developing a new structure for silicon modulators to improve the performance is a crucial research direction in silicon photonics. Meanwhile, slow-light effect is an important physical phenomenon that can reduce the group velocity of light. Applying slow-light effect on silicon modulators through photonics crystal and waveguide grating structures is an attractive research point, especially in the aspect of reducing the device footprint. In this paper, we review the recent progress of silicon-based slow-light electro-optic modulators towards future communication requirements. Beginning from the principle of slow-light effect, we summarize the research of silicon photonic crystal modulators and silicon waveguide grating modulators in detail. Simultaneously, the experimental results of representative silicon slow-light modulators are compared and analyzed. Finally, we discuss the existing challenges and development directions of silicon-based slow-light electro-optic modulators for the practical applications

Evaluating the Robustness to Instructions of Large Language Models

Recently, Instruction fine-tuning has risen to prominence as a potential method for enhancing the zero-shot capabilities of Large Language Models (LLMs) on novel tasks. This technique has shown an exceptional ability to boost the performance of moderately sized LLMs, sometimes even reaching performance levels comparable to those of much larger model variants. The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks. We conducted an exploration of six models including Alpaca, Vicuna, WizardLM, and Traditional Task-oriented Models(Flan-T5-XL/XXL, T0++) using real-world relation extraction datasets as case studies. We carried out a comprehensive evaluation of these instruction-following LLMs which have been tuned based on open-domain instructions and task-oriented instructions. The main discussion is their performance and robustness towards instructions. We have observed that in most cases, the model's performance in dealing with unfamiliar instructions tends to worsen significantly, and the robustness of the model for RE instructions deteriorates compared to QA. Further, we discovered that up until a certain parameter size threshold (3B), the performance of the FLAN-T5 model improves as the parameter count increases. The robustness of different scales of FLAN-T5 models to RE instruction is worse than the robustness to QA instruction.Comment: In our study, erroneous data analysis inadvertently led to misleading outcomes. Incorrect variables were included, distorting results. This emphasizes the significance of robust data processing and analysis techniques in researc

The first geodetic investigation at the summit of Dome A, Antarctica

Dome A is the highest ice feature in the Antarctica, up to now, little is known about surface topography at Dome A. The first Chinese ITASE expedition was carried out from Zhongshan station to Dome A during the 1996/1997 austral summer. During the 2004/2005 austral summer, the traverse was extended to the summit of Dome A which is 1228 km from Zhongshan Station by 21st Chinese National Antarctic Research Expedition (CHINARE). The real-time kinematic (RTK) GPS survey was carried out in the summit of Dome A during 2004/2005 austral summer. The surface topography of Dome A was drawn up using the kinematic double-frequency GPS data covering an area of about 70 km2. The accuracy of the kinematic survey is in the range of 0.20 m. Precise surface topography, bedrock morphology and internal layering geometry are important for the future selection of the best site for deep drilling at Dome A