Miracle-dz/UniFAR-FTUs huggingface dataset:
SnRNA-Seq of Pancreas Revealed the Dysfunction of Endocrine and Exocrine Cells in Transgenic Pigs with Prediabetes

Diabetes poses a significant threat to human health. Exocrine pancreatic dysfunction is related to diabetes, but the exact mechanism is not fully understood. This study aimed to describe the pathological phenotype and pathological mechanisms of the pancreas of transgenic pigs (PIGinH11) that was constructed in our laboratory and to compare it with humans. We established diabetes-susceptible transgenic pigs and subjected them to high-fat and high-sucrose dietary interventions. The damage to the pancreatic endocrine and exocrine was evaluated using histopathology and the involved molecular mechanisms were analyzed using single-nucleus RNA-sequencing (SnRNA-seq). Compared to wild-type (WT) pigs, PIGinH11 pigs showed similar pathological manifestations to type 2 diabetes patients, such as insulin deficiency, fatty deposition, inflammatory infiltration, fibrosis tissue necrosis, double positive cells, endoplasmic reticulum (ER) and mitochondria damage. SnRNA-seq analysis revealed 16 clusters and cell-type-specific gene expression characterization in the pig pancreas. Notably, clusters of Ainar-M and Endocrine-U were observed at the intermediate state between the exocrine and endocrine pancreas. Beta cells of the PIGinH11 group demonstrated the dysfunction with insulin produced and secret decreased and ER stress. Moreover, like clinic patients, acinar cells expressed fewer digestive enzymes and showed organelle damage. We hypothesize that TXNIP that is upregulated by high glucose might play an important role in the dysfunction of endocrine to exocrine cells in PIGinH11 pigs.
Personalized Interventional Surgery of the Lumbar Spine: A Perspective on Minimally Invasive and Neuroendoscopic Decompression for Spinal Stenosis

Pain generator-based lumbar spinal decompression surgery is the backbone of modern spine care. In contrast to traditional image-based medical necessity criteria for spinal surgery, assessing the severity of neural element encroachment, instability, and deformity, staged management of common painful degenerative lumbar spine conditions is likely to be more durable and cost-effective. Targeting validated pain generators can be accomplished with simplified decompression procedures associated with lower perioperative complications and long-term revision rates. In this perspective article, the authors summarize the current concepts of successful management of spinal stenosis patients with modern transforaminal endoscopic and translaminar minimally invasive spinal surgery techniques. They represent the consensus statements of 14 international surgeon societies, who have worked in collaborative teams in an open peer-review model based on a systematic review of the existing literature and grading the strength of its clinical evidence. The authors found that personalized clinical care protocols for lumbar spinal stenosis rooted in validated pain generators can successfully treat most patients with sciatica-type back and leg pain including those who fail to meet traditional image-based medical necessity criteria for surgery since nearly half of the surgically treated pain generators are not shown on the preoperative MRI scan. Common pain generators in the lumbar spine include (a) an inflamed disc, (b) an inflamed nerve, (c) a hypervascular scar, (d) a hypertrophied superior articular process (SAP) and ligamentum flavum, (e) a tender capsule, (f) an impacting facet margin, (g) a superior foraminal facet osteophyte and cyst, (h) a superior foraminal ligament impingement, (i) a hidden shoulder osteophyte. The position of the key opinion authors of the perspective article is that further clinical research will continue to validate pain generator-based treatment protocols for lumbar spinal stenosis. The endoscopic technology platform enables spine surgeons to directly visualize pain generators, forming the basis for more simplified targeted surgical pain management therapies. Limitations of this care model are dictated by appropriate patient selection and mastering the learning curve of modern MIS procedures. Decompensated deformity and instability will likely continue to be treated with open corrective surgery. Vertically integrated outpatient spine care programs are the most suitable setting for executing such pain generator-focused programs.
Individual Monitoring of Activity and Lameness in Conventional and Slower-Growing Breeds of Broiler Chickens Using Accelerometers

Simple Summary Broiler chickens are specifically bred for meat, and conventional breeds have been genetically selected for faster growth rates. Rapid growth is associated with a greater risk of lameness in broiler chickens, which is a major welfare concern associated with an economic loss for producers. Although defined as an individual’s physical and mental state, welfare is often monitored at the group level and is at risk of overlooking individual issues. Body-mounted accelerometers measure movement and could potentially monitor individual welfare, including lameness. However, body-mounted sensors may alter behavior and/or impact welfare. We investigated relationships between accelerometer-recorded activity (all movement), weight, and lameness in broiler breeds with different growth rates. Wearing the device did not negatively impact lameness, and, except for preening more, birds behaved similarly to those not wearing accelerometers after 24 h. We found that faster-growing, heavier birds were less active compared to slower-growing, lighter birds, demonstrating that accelerometers could detect known influences on activity useful for general monitoring, but that accelerometer-detected activity was not linearly associated with lameness. Further research on accelerometer detection of more specific behavioral changes likely to be associated with lameness, such as non-linear acceleration with gait score, offers the possibility of better continuous monitoring of individuals to assess lameness objectively. Abstract Accelerometers are increasingly being investigated to detect animal behavior as a method for monitoring individual welfare that overcomes manual challenges associated with time, resource, and discrete sampling. We investigated the effects of broiler chicken hybrid (hereafter breed) and weight on accelerometer activity (activityA; calculated as percentage of time spent active (%)) and its association with lameness as a major broiler welfare concern. Accelerometers were attached to birds of different breeds on between 2 and 4 occasions from 26 to 30 days old (conventional breed CNV) and 26 to 49 days old (two slower-growing breeds SGH; SGN). At 2.2 kg, lameness was scored using a 6-point gait scoring system (0: unaffected to 5: severely lame). Linear mixed effects models and breed-stratified generalized linear models together with a random-effect meta-analysis were used for data analyses. ActivityA was lower in faster-growing, heavier birds compared to slower-growing, lighter birds, showing overall consistency with previous behavioral research, but did not vary linearly with gait score. Accelerometers offer the potential for simple broad-scale continuous monitoring of broiler chicken activity behavior that requires limited data processing. Exploration of the ability of accelerometers to capture more subtle and specific changes in behavioral patterning, such as non-linear acceleration with gait score that could indicate early development of lameness, warrants further investigation.
Antimicrobial and in vitro cytotoxicity effect of green synthesized silver nanoparticles using aqueous leaf extract of Senna hirsuta

In this study, we prepared silver nanoparticles (AgNPs) using an aqueous Senna hirsuta leaf extract. The AgNPs were characterized using UV visible spectrophotometer, particle size, zeta potential, energy diffraction X-ray (EDAX), Fourier transform infrared (FTIR), transmission electron microscopy (TEM) and determined antibacterial and cytotoxicity impact of the AgNPs. UV-Visible spectroscopy of Senna hirsuta leaf extract synthesized into AgNPs was carried out at various time intervals. The peak (λ max) was observed at 436–446 nm and it was broad. The particle size of the synthesized AgNPs was found 28.3 nm. The Zeta potential of AgNPs was found -4.4 mV. TEM analysis demonstrated that nanoparticles have a spherical form. According to the EDAX analysis, the extract effectively capped the AgNPs. Zone of inhibition of AgNPs were observed more than those of the extract. Silver nanoparticles demonstrated effective antimicrobial activity due to their large surface area, which allows for stronger contact with cell wall of bacteria. In vitro cytotoxicity showed that synthesized AgNPs a prominent cytotoxicity activity against MCF7 cell lines.
Synchronous slew/translation positioning and swing suppression control for 4-DOF tower crane system

This paper introduces a method to navigate the tower crane movement and suppress the payload swing synchronously. Special error signals are introduced, which bring the coupling of dynamics among various states of the crane and enable the control to meet the synchronous operation in practice. The special angle-dependent energy functions are designed as the candidate sub-Lyapunov functions for decoupling the states. The controller is then stepwise designed with the help of the dynamics model. The stability of the control is proven in the Lyapunov sense. Extensive simulations and experiments have been carried out to demonstrate the effectiveness of the proposed nonlinear coupling control. A popular control approach in the literature is chosen to compare with the current work. It is found that the proposed control is quite effective in suppressing the payload swing while tracking the pre-determined path at the same time.
Submicron-Grooved Films Modulate the Directional Alignment and Biological Function of Schwann Cells

Topographical cues on material surfaces are crucial for guiding the behavior of nerve cells and facilitating the repair of peripheral nerve defects. Previously, micron-grooved surfaces have shown great potential in controlling nerve cell alignment for studying the behavior and functions of those cells and peripheral nerve regeneration. However, the effects of smaller-sized topographical cues, such as those in the submicron- and nano-scales, on Schwann cell behavior remain poorly understood. In this study, four different submicron-grooved polystyrene films (800/400, 800/100, 400/400, and 400/100) were fabricated to study the behavior, gene expression, and membrane potential of Schwann cells. The results showed that all submicron-grooved films could guide the cell alignment and cytoskeleton in a groove depth-dependent manner. Cell proliferation and cell cycle assays revealed that there was no significant difference between the submicron groove samples and the flat control. However, the submicron grooves can direct the migration of cells and upregulate the expression of critical genes in axon regeneration and myelination (e.g., MBP and Smad6). Finally, the membrane potential of the Schwann cells was significantly altered on the grooved sample. In conclusion, this study sheds light on the role of submicron-grooved patterns in regulating the behavior and function of Schwann cells, which provides unique insights for the development of implants for peripheral nerve regeneration.
H∞ filtering for continuous fractional‐order 2D Roesser model: The 
 0

This paper investigates the H∞$$ {H}_{\infty } $$ filtering for the continuous fractional‐order (FO) two‐dimensional (2D) Roesser model with the FO between 0 and 1. Firstly, a sufficient condition to ensure the stability and bounded realness in the sense of H∞$$ {H}_{\infty } $$ ‐norm for the continuous FO 2D Roesser model is given in the form of linear matrix inequalities (LMIs). Secondly, based on the bounded real lemmas proposed above, the H∞$$ {H}_{\infty } $$ filtering problem for continuous FO 2D Roesser model is addressed through some congruent transformation and matrix transformation. The results are given in LMI form, and the parameters of the continuous FO 2D filters can be achieved from the LMIs easily. In the end, the effectiveness of the proposed results is verified by two numerical examples.
On subgroups of the Dixmier group and Calogero-Moser spaces

We describe the structure of the automorphism groups of algebras Morita equivalent to the first Weyl algebra $ A_1(k) $. In particular, we give a geometric presentation for these groups in terms of amalgamated products, using the Bass-Serre theory of groups acting on graphs. A key role in our approach is played by a transitive action of the automorphism group of the free algebra $ k $ on the Calogero-Moser varieties $ \CC_n $ defined in [5]. In the end, we propose a natural extension of the Dixmier Conjecture for $ A_1(k) $ to the class of Morita equivalent algebras.
Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation

Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Robotic Network (AERONET) produced nighttime AOD, called lunar AOD, providing an opportunity to estimate nighttime PM. Nighttime AOD measurements are particularly important as they help fill gaps in our understanding of aerosol variability and its impact on the atmosphere, as there are significant variations in AOD between day and night. In this study, the relationship between lunar AOD and PM was investigated using data from AERONET station, meteorological station, and air pollution station in Seoul Metropolitan area from May 2016 to December 2019, and then PM estimation model was developed covering both daytime and nighttime using random forest machine learning techniques. We have found the differences in the importance of variables affecting the AOD-PM relationship between day and night from the random forest model. The AOD-PM relationship in the daytime was more affected by time-related variables, such as the day of the year among the variables. The new model was developed using additional lunar AOD data to estimate continuous PM concentrations. The results have shown that the model based on lunar AOD data estimated well PM10 and PM2.5 with similar performance of model using solar AOD. The results imply the possibility of seamless near-surface PM concentration data on a large scale once satellites produce nighttime AOD data.
High-Sucrose Diet Exposure on Larvae Contributes to Adult Fecundity and Insecticide Tolerance in the Oriental Fruit Fly, Bactrocera dorsalis (Hendel)

Simple Summary The oriental fruit fly, Bactrocera dorsalis, is a highly significant invasive pest in agriculture, causing damage to over 600 species of fruits and vegetables. To gain a better understanding of its host adaptation and rapid spread, it is crucial to investigate the effects of changes in nutrient content on the phenotype and gene expression of B. dorsalis. In this study, we examined the developmental duration, adult fecundity, insecticide susceptibility, and gene expression patterns of B. dorsalis by varying the concentration of sucrose in its larval diet. Our findings indicate that a high-sucrose diet during larval stages resulted in a longer developmental period, higher adult fecundity, and greater tolerance to malathion. In contrast, a low-sucrose diet led to smaller body size, shorter developmental duration, and higher sensitivity to beta-cypermethrin. Additionally, we identified differentially expressed genes associated with various metabolisms, hormone synthesis and signaling, and immune-related pathways under different sucrose concentrations in the larval diet. These results suggest that dietary sucrose plays a significant role in phenotypic adjustments and gene expression patterns in B. dorsalis. Abstract Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) is one of the broad host ranges and economically-important insect pests in tropical and subtropical areas. A wide range of hosts means they have strong adaptation ability to changes in dietary macronutrients (e.g., sucrose and protein). However, the effects of dietary conditions on the phenotypes and genotypes of B. dorsalis are still unclear. In this study, we aimed to investigate the effects of larval dietary sucrose on the life history traits and stress tolerance of B. dorsalis, and its defense response at the molecular level. The results showed that low-sucrose (LS) induced decreased body size, shortened developmental duration, and enhanced sensitivity to beta-cypermethrin. Otherwise, high-sucrose (HS) diet increased developmental duration, adult fecundity, and tolerance to malathion. Based on transcriptome data, 258 and 904 differentially expressed genes (DEGs) were identified in the NS (control) versus LS groups, and NS versus HS groups, respectively. These yielded DEGs were relevant to multiple specific metabolisms, hormone synthesis and signaling, and immune-related pathways. Our study will provide biological and molecular perspective to understand phenotypic adjustments to diets and the strong host adaptability in oriental fruit flies.
Analysis of Generalized Maximum Entropy Regression Effect

Aiming at generalized maximum entropy(GME) regression effect and especially the indetermination of the choice of support space of parameter and error in the model,the modelling process of GME regression method is analyzed,and its regression effect is compared with others' effects through two cases in this paper.Results show that the forecasting precision and the explaining ability of GME method is higher than the least square method,the partial least square method and the principal component analysis.The larger the support space of parameter is,the better,under the lack of prior information,and the support space of error should be between 3σ and 4σ.
Flood prediction using machine learning

Abstract: The most frequent natural disaster in the world, flooding affects hundreds of millions of people and kills between6,000 and 18,000 people annually, with 20% of those deaths occurring in India. Several people lack access to reliable early warning systems, despite the fact that those systems already exists demonstrated may avoid an large portion of economy anddeath loss. Improved performance and cost-effective solutions are offered by this prediction system's development. In order toforecast the occurrence of floods brought on by rainfall, a prediction model is created in this article. Based on the rainfall range for certain places, the model forecasts if "flood mayhappen or not". information about rainfall in Indian districts.
Neural Network based CPG Control Method of Undulating Fin in Underwater Biomimetic Robot

This paper presents a deep neural network (DNN) based mapping and inverse mapping method to control an undulating fin developed in soft material. The undulating fin can reproduce the periodic biological motion by applying a central pattern generator (CPG) oscillating model and constructing networks between oscillators. Due to the dynamic complexity of the soft material and the dependency on the CPG oscillator, the dynamic model of the system has complex and highly non-linear properties. Here, to model the complex non-linear relationship between CPG control input and output thrust, we propose two DNN models for mapping (recognition network) and inverse mapping (generation network). Water tank experiments were conducted to obtain the output thrust according to the various CPG inputs, and the experimental data were used for learning the proposed networks. After learning, we compared the accuracy of the output thrust estimation between the proposed mapping method using the recognition network and the existing linear regression method. The tendency of CPG input recommendation using the generation network was also confirmed. Based on the proposed approach, we designed a robot control methodology integrating the proposed DNN and CPG network.
Green resource orchestration: A critical appraisal of the use of resource orchestration in environmental management research, and a research agenda for future study

Resource orchestration (RO) is a strategic management framework that details various processes by which how firms structure, bundle, and leverage resources. This study reviews how RO has been considered in environmental management research. The review highlights some limitations on how RO has been used in studies on environmental management, and it also illustrates a lack of knowledge accumulation in the field. To realize the full potential of RO, the concept of green RO is developed. Green RO (GRO) is defined as a firm's capability to coordinate and manage the structuring of resources and the bundling and leveraging of capabilities to create economic value for the company while simultaneously creating environmental benefits. Three main research areas for GRO are described: GRO as an organizational or managerial meta-capability, management of specific GRO processes, and the synchronization of several GRO processes.
SPOT (Sales Production based On Time-Series): A Comprehensive Approach to Sales Forecasting using Contextually-tailored Time Series Analysis

Unit sales in retail chain stores exhibit a significant degree of variance, affected by seasonality and special events. Individual products and categories also display a considerable difference in sales based on the geography of individual stores. In various businesses, a myriad of products is moved each day, and accurately predicted knowledge of sales facilitates the planning of logistics, warehousing, and procurement, besides other executive decision-making processes. Software developed for this purpose are undergoing continuous improvements. SPOT (Sales Production based On Time-Series) is an application to produce sales forecasts for customized input parameters – such as an individual store, state in which store is located, a particular product, category/sub-category of products, which will prove to be immensely useful for decision-making processes across a variety of concerned business units. SPOT delivers the results in the form of graphical summarization of selected data, along with its time-series forecasts. More incisive sales predictions can be exported as a tabular numeric file. For the purpose of demonstrating the entire process, data from Walmart was considered; however, this can be extended to any related application fields as well.
A Dual-Mode Neural Stimulator with Two-Step Charge Balancing Scheme for Fast Residual Voltage Compensation

Recent advance in bi-directional neural interface requires miniaturized neural stimulator for implantable applications. Charge balancing circuit is needed to minimize residual electrode voltage after stimulation, hereby avoid tissue damage in long-term usage. This manuscript proposed a dual-mode neural stimulator with charge balancing circuits. With combined continuous current and discrete current pulse compensation techniques, the compensation time is minimized without degradation of accuracy and hardware overhead. The proposed system is implemented in a typical $0.18\upmu \mathrm{m}$ HV process, with integrated high voltage generator. The stimulator was able to deliver 14.4 V pulse and 3 mA current. Compared with conventional charge balancing method, the implemented system achieved 50% compensation time saving with power consumption lower than $18 \upmu \mathrm{W}$ and final residual voltage lower than 7 mV.
An approach to time domain optimization of observer-based fault detection systems

This contribution deals with the problems related to robust fault detection. An approach to time domain optimization of observer-based fault detection systems is proposed. The core of this approach is the integration of robust residual generation and evaluation which provides a time domain optimal solution for the robustness problem of fault detection. The discussion on the optimal solution reveals some potential possibilities and ideas for improving the performance of fault detection systems.