bluuebunny/crossref_metadata_2025 huggingface dataset:
Production of Sustainable Proteins Through the Conversion of Insects to Proteins Using Beauveria bassiana Cultures
Authors: Gutiérrez Román Ana I. F. , Laynes Zela Pedro F. , Acuña Payano Rosalyn K. , Nolasco Cárdenas Oscar P. , Santa-Cruz Carpio Carlos M. , Leiva Eriksson Nélida R.
Published: January 2022

Various strategies are being suggested to solve the challenges in the food system, such as changing the source of nutrients, including the use of non-traditional food sources such as insects. Although insects are promoted as a cheap and sustainable source of protein, consumers are reluctant to eat them. The mycoproteins produced by fungi, on the other hand, are very well received and appreciated by consumers. Thus, in this work we have studied the use of the entomopathogenic fungi Beauveria bassiana (Ascomycota: Hypocreales) for the production of protein using insects as feed. B. bassiana was cultivated in culture medium containing entire insects from the species Eurysacca and Hypothenemus or single carbon sources such as glucose or laminarin from Laminaria digitata. The results showed that B. bassiana can produce up to 16-fold more biomass and 8-fold more protein when grown in insect-based medium than when grown in glucose. The results also indicated that the production of proteins continuously increased when B. bassiana was grown in medium containing insects, reaching its maximum at 9 days (up to 3 mg/mL). On the other hand, when cultivated in glucose-supplemented medium, the production of proteins was constantly low (~0.5 mg/mL). In conclusion, B. bassiana was a large biomass producer and exuded a large amount of protein when grown in medium containing insect powder, making it an ideal intermediate link between insects and protein. Furthermore, the proteins produced by fungi such as B. bassiana can be used in the food, health, and cosmetic industries.
Evaluation of Bone Wax Coated Bipolar Coagulation Forceps: Performance and Safety Assessment
Authors: Shi Jichun , Wei Wei , Wang Zhen , Ren Haobin , Jia Chenguang , Dong Lixin , Li Zhengwei , Zhang Jianjian , Feng Yu , Huang Kaixin , Li Xiang , Chen Jincao
Published: January 2022

Background

Improving the performance of bipolar coagulation forceps is crucial for safer and more accurate neurosurgery. In our department, we found that bone wax (BW) melted by thermal effect of bipolar electrocoagulation can achieve more efficient hemostasis and reduce the amount of BW in neurosurgical procedures associated with bleeding from emissary and diploic veins. Nevertheless, relevant studies are still lacking to verify our finding.

Objective

The study objectives were to evaluate the performance and safety in electrocoagulation: (1) compare the performance of BW coated bipolar coagulation forceps and the conventional anti-stick forceps in vivo, and (2) assess the safety of electrocoagulation with BW coated bipolar coagulation forceps in rat primary motor cortex.

Methods

Tissue adhesion was evaluated by comparing the wetting tension and the amount of protein adhered to the forceps tips after electrocoagulation. Thermal damage was assessed by analyzing the thermography and H&E staining of coagulated rat brain tissues. The hemostatic efficiency was reflected by the number of electrocoagulation until complete hemostasis and the condition of damaged common carotid arteries. The safety of BW coated forceps in electrocoagulation was assessed by evaluating the inflammation of coagulated rat primary motor cortex and the motor functions at the 7th day postoperatively.

Results

Bone wax coated forceps had a significantly higher contact angle and adhered less coagulum. Thermography was acquired at 3 s, 6 W units in rat primary motor cortex in vivo. The highest temperature recorded during BW coated tips application was significantly lower than the uncoated. In addition, there was a relatively smaller tissue injury area produced by the BW coated forceps. Additionally, BW coated forceps improved the hemostatic efficiency and caused fewer injuries on the damaged arteries (3 s, 10 W units). More importantly, electrocoagulation with BW coated forceps led to no significant motor function impairments and less glial and microglia responses.

Conclusion

This study reveals that BW coated bipolar coagulation forceps can provide a convenient, cost-efficient, safer, and more efficient way for hemostasis. More research is needed to evaluate the electrocoagulation with BW in the long term and verify our finding in human beings.
Association Between Preoperative Blood Glucose Level and Hospital Length of Stay in Patients With Kidney Stones Undergoing Percutaneous Nephrolithotomy
Authors: Sun Si , Mao Weipu , Tao Shuchun , Wan Lilin , Zou Xiangyu , Zhang Guangyuan , Chen Ming
Published: January 2022

Aim

To assess the effect of preoperative blood glucose (POBG) levels on the length of stay (LOS) in patients with kidney stones undergoing percutaneous nephrolithotomy (PCNL).

Methods

We conducted a retrospective study of patients who underwent PCNL at the Zhongda Hospital of Southeast University from 2013 to 2019. The relationship between POBG level and LOS was investigated by dose-response analysis curves of restricted cubic spline function.

Results

We included 310 patients and divided them into three groups (<5.04, 5.04 to <6.88, ≥6.88 mmol/L) according to the POBG levels. Patients with POBG levels ≥6.88 mmol/L (adjusted odds risk [aOR] 1.67; 95% CI 0.83–3.33) had a 67% higher risk of LOS > 2 weeks than patients with POBG levels <5.04 mmol/L. A positive dose-response analysis curve was observed between POBG and the adjusted risk of LOS >2 weeks. Similar results were observed in the subgroups analysis.

Conclusion

We demonstrated that higher POBG levels were significantly associated with longer LOS in patients with kidney stones undergoing PCNL.
Impact of a Dietary Citrus Extract on the Behavior and Production of Lactating Dairy Cows Following Regrouping: A Preliminary Study
Authors: Padua Felipe H. , Dancy Kaitlyn M. , Bergeron Renée , DeVries Trevor J.
Published: January 2022

Exposure of lab animals, humans, and pigs to olfactory sensory feed additives may reduce response to stress and anxiety. The objective of this preliminary study was to determine if feeding a citrus-based olfactory sensory functional feed extract (derived fromCitrus sinensis) reduces the negative impact of regrouping of lactating dairy cows. Thirty-two (parity = 2.0 ± 1.2; mean ± SD), mid-lactation Holstein dairy cows (169.8 ± 16.8 DIM) were enrolled as focal cows in this study and housed individually in a tie-stall facility where they were randomly assigned to 1 of 2 treatment diets: (1) control total mixed ration (TMR) (control;n= 16; primiparous = 7; multiparous = 9), or (2) control TMR with 4 g/d of citrus extract (CE) (Phodé, Terssac, France) (CE;n= 16; primiparous = 7; multiparous = 9). Cows were fed their experimental diets for 7 d in the tie-stall facility (baseline), then moved to 1 of 2 experimental free-stall pens (containing 29 other cows) for a period of 7 d, where they remained on the same treatment diet as before. Compared with their baseline, primiparous control cows had decreased rumination time on d 1 and 2, had decreased lying time on d 1, and tended to have decreased lying time on d 2 and 3 following regrouping. In contrast, primiparous cows fed the CE diet did not experience a change in rumination and lying time. Primiparous CE cows had greater feeding time on d 1 and tended to have greater feeding time on d 2 after regrouping compared to primiparous control cows. Primiparous control cows had greater idle standing time, as compared to the CE cows, across the 7 d after regrouping. Primiparous CE cows initiated less total competitive behavior after regrouping, but were also displaced more frequently from the feed bunk and from the free stalls on d 1 after regrouping, as compared to the primiparous control cows. For multiparous cows, CE supplementation was not consistently associated with any benefits to behavior or production after regrouping, possibly because these cows were more experienced with social stressors. Results indicate that feeding CE to mid-lactation naïve primiparous dairy cows may reduce the initiation of competitive interactions and lessen the reduction in rumination and lying time after regrouping. These results need to be verified in further studies where potential confounding effects (e.g., pen social dynamics, pen location) are minimized.
Perspective: Re-defining “Pheromone” in a Mammalian Context to Encompass Seminal Fluid
Authors: Robertson Sarah A. , Martin Graeme B.
Published: January 2022

The classical view of “pheromone”—an air-borne chemical signal—is challenged by the camelids in which ovulation is triggered by ß-nerve growth factor carried in seminal plasma, effectively extending the pheromone concept to a new medium. We propose further extension of “pheromone” to include a separate class of seminal fluid molecules that acts on the female reproductive tract to enhance the prospect of pregnancy. These molecules include transforming growth factor-ß, 19-OH prostaglandins, various ligands of Toll-like receptor-4 (TLR4), and cyclic ADP ribose hydrolase (CD38). They modulate the immune response to “foreign” male-derived histocompatibility antigens on both sperm and the conceptus, determine pre-implantation embryo development, and then promote implantation by increasing uterine receptivity to the embryo. The relative abundance of these immunological molecules in seminal plasma determines the strength and quality of the immune tolerance that is generated in the female. This phenomenon has profound implications in reproductive biology because it provides a pathway, independent of the fertilizing sperm, by which paternal factors can influence the likelihood of reproductive success, as well as the phenotype and health status of offspring. Moreover, the female actively participates in this exchange—information in seminal fluid is subject to “cryptic female choice,” a process by which females interrogate the reproductive fitness of prospective mates and invest reproductive resources accordingly. These processes participate in driving the evolution of male accessory glands, ensuring optimal female reproductive investment and maximal progeny fitness. An expanded pheromone concept will avoid a constraint in our understanding of mammalian reproductive biology.
The Pressing Issue of Micro- and Nanoplastic Contamination: Profiling the Reproductive Alterations Mediated by Oxidative Stress
Authors: Ferrante Maria Carmela , Monnolo Anna , Del Piano Filomena , Mattace Raso Giuseppina , Meli Rosaria
Published: January 2022

Micro- and nanoplastics (MPs/NPs) are among the most widely distributed pollutants in the environment. It has been suggested that exposure to MPs/NPs can trigger toxicity pathways among which inflammation and oxidative stress (OS) play a pivotal role. Once absorbed, MPs/NPs may act locally or access the bloodstream and, following the translocation process, reach several organs and tissues, including the gonads. Notably, MPs/NPs can bioaccumulate in human and murine placenta, opening new scenarios for toxicological evaluations. We review recent studies on the effects of MPs/NPs on the reproductive health in aquatic and terrestrial organisms of both sexes, focusing on the role of OS and the antioxidant defence system failure as the main underlying mechanisms. Alterations in gametogenesis, embryonic and offspring development, and survival have been shown in most studies and often related to a broken redox balance. All these detrimental effects are inversely related to particle size, whereas they are closely linked to shape, plastic polymer type, superficial functionalization, concentration, and time of exposure. To date, the studies provide insights into the health impacts, but no conclusions can be drawn for reproduction toxicity. The main implication of the few studies on antioxidant substances reveals their potential role in mitigating MP-induced toxic effects.
A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
Authors: Vosta Soheil , Yow Kin-Choong
Published: January 2022

Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events in the camera feeds due to human error. Therefore, monitoring surveillance cameras could be a waste of time and energy. On the other hand, many researchers worked on surveillance data and proposed several methods to detect abnormal events automatically. As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately. Therefore, we introduced a model for detecting abnormal events in the surveillance camera feed. In this work, we designed a model by implementing a well-known convolutional neural network (ResNet50) for extracting essential features of each frame of our input stream followed by a particular schema of recurrent neural networks (ConvLSTM) for detecting abnormal events in our time-series dataset. Furthermore, in contrast with previous works, which mainly focused on hand-crafted datasets, our dataset took real-time surveillance camera feeds with different subjects and environments. In addition, we classify normal and abnormal events and show the method’s ability to find the right category for each anomaly. Therefore, we categorized our data into three main and essential categories: the first groups mainly need firefighting service, while the second and third categories are about thefts and violent behaviour. We implemented the proposed method on the UCF-Crime dataset and achieved 81.71% in AUC, higher than other models like C3D on the same dataset. Our future work focuses on adding an attention layer to the existing model to detect more abnormal events.
Mechanical Properties of Lamellar Shale Considering the Effect of Rock Structure and Hydration from Macroscopic and Microscopic Points of View
Authors: Zhang Qiangui , Wang Lizhi , Zhao Pengfei , Fan Xiangyu , Zeng Feitao , Yao Bowei , He Liang , Yang Simin , Feng Yang
Published: January 2022

It is well known that the effect of shale hydration causes wellbore instability due to water phase invasion of drilling fluid to lamellar shale rich formations. This is because of that the mechanical properties (compressive strength, elastic modulus, etc.) of lamellar shale surrounding the borehole, which is rich in clay minerals, will decrease significantly after hydration. In this study, using the lamellar shale in the continental stratum of the southern Ordos Basin, the mechanical properties of lamellar shale were studied by compression tests considering the effect of lamellar structure and hydration from a macroscopic point of view. In addition, the mechanical mechanism was discussed combined with the CT scanning tests results from a microscopic point of view. The results demonstrate the following points. Lamellar shale has stronger anisotropy than bedding shale, the compressive strength (deviatoric stress) and elastic modulus of lamellar shale are both lower than those of bedding shale, and it is more prone to tension fracture. With the increase in the angle (β) between the lamina and the axial direction from 0° to 90°, the compressive strength of lamellar shale decreases when β < 30° and then increases, the elastic modulus of lamellar shale decreases greatly when β < 30° and then tends to flatten. With the increase in hydration time, the compressive strength and elastic modulus of lamellar shale both gradually decrease, and the rates of their decrements reduce. The mechanical properties of lamellar shale are more affected by hydration than those of bedding shale. The hydration of lamellar shale leads to the formation of new fractures and the expansion of existing fractures in the junction area between the laminae and rock matrix, resulting in easy tension fracture along the laminae of shale.
A Porous Tungsten Substrate for Catalytic Reduction of Hydrogen by Dealloying of a Tungsten–Rhenium Alloy in an Aqueous Solution of Hydrochloric Acid
Authors: Chernyshev Aleksander A. , Nikitina Evgenia V.
Published: January 2022

Selective dissolution of a tungsten (85 wt.%)–rhenium (15 wt.%) alloy with rhenium in hydrochloric acid at the temperature of 298 K and anodic polarization modes was carried out to develop a porous catalytic substrate and to recycle rare metals. The parameters of the effective selective anodic dissolution of the tungsten–rhenium alloy, including the differences in applied potentials and electrolyte composition, were found. It was established that samples of the tungsten–rhenium alloy possess the smallest average pore size after being exposed for 6000 s. The obtained porous tungsten samples were characterized by X-ray diffraction and scanning electron spectroscopy. A thermodynamic description of the processes occurring during the anodic selective dissolution of a binary alloy was proposed. In the course of the work, the selectivity coefficient was determined using an X-ray fluorescence wave-dispersion spectrometer XRF-1800. The existence of a bimodal structure on the tungsten surface after dealloying was proved.
Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation
Authors: Park Soon-Young , Dash Uzzal Kumar , Yu Jinhyeok
Published: January 2022

Data assimilation (DA) combines incomplete background values obtained via chemical transport model predictions with observational information. Several 3-Dimensional variational (3DVAR) and sequential methods (e.g., ensemble Kalman filter (EnKF)) are used to define model errors and build a background error covariance (BEC) and are important factors affecting the prediction performance of DA. The BEC determines the spatial range, where observation concentration is reflected in the model when DA is applied to an air pollution transport model. However, studies investigating the characteristics of BEC using air quality models remain lacking. In this study, horizontal length scale (HLS) and vertical length scale (VLS) analyses of a BEC were applied to EnKF and ensemble square root filter (EnSRF), respectively, and two ensemble-based DA methods were performed; the characteristics were compared with those of a BEC applied to 3DVAR. The results of 6 h PM2.5 predictions performed for 42 days were evaluated for a control run without DA (CTR), 3DVAR, EnKF, and EnSRF. HLS and VLS respectively exhibited a high correlation with the ground wind speed and with the planetary boundary layer height for diurnal and daily variations; EnKF and EnSRF exhibited superior performances among all the methods. The root mean square errors were 11.9 μg m−3 and 11.7 μg m−3 for EnKF and EnSRF, respectively, while those for 3DVAR and CTR were 12.6 μg m−3 and 18.3 μg m−3, respectively. Thus, we proposed a simple method to find a Gaussian function that best described the error correlation of the BEC based on the physical distance.
Bcl-2 Family Members and the Mitochondrial Import Machineries: The Roads to Death
Authors: Lalier Lisenn , Vallette François , Manon Stéphen
Published: January 2022

The localization of Bcl-2 family members at the mitochondrial outer membrane (MOM) is a crucial step in the implementation of apoptosis. We review evidence showing the role of the components of the mitochondrial import machineries (translocase of the outer membrane (TOM) and the sorting and assembly machinery (SAM)) in the mitochondrial localization of Bcl-2 family members and how these machineries regulate the function of pro- and anti-apoptotic proteins in resting cells and in cells committed into apoptosis.
Spreading of Aggregated α-Synuclein in Sagittal Organotypic Mouse Brain Slices
Authors: Uçar Buket , Stefanova Nadia , Humpel Christian
Published: January 2022

The accumulation of α-synuclein (α-syn) in the brain plays a role in synucleinopathies and it is hypothesized to spread in a prion-like fashion between connected brain regions. In the present study, we aim to investigate this spreading in well-characterized sagittal organotypic whole brain slices taken from postnatal wild type (WT) and transgenic mice overexpressing human α-syn under the promoter of proteolipid protein (PLP). Collagen hydrogels were loaded with monomers of human α-syn, as well as human and mouse pre-formed fibrils (PFFs), to allow local application and slow release. The spreading of α-syn was evaluated in different brain regions by immunohistochemistry for total α-syn and α-syn phosphorylated at the serine129 position (α-syn-P). The application of human and mouse PFFs of α-syn caused the aggregation and spreading of α-syn-P in the brain slices, which was pronounced the most at the region of hydrogel application and surrounding striatum, as well as along the median forebrain bundle. The organotypic slices from transgenic mice showed significantly more α-syn pathology than those from WT mice. The present study demonstrates that seeding with α-syn PFFs but not monomers induced intracellular α-syn pathology, which was significantly more prominent in brain slices with α-syn overexpression. This is consistent with the prion-like spreading theory of α-syn aggregates. The sagittal whole brain slices characterized in this study carry the potential to be used as a novel model to study α-syn pathology.
Backward Locomotor Treadmill Training Differentially Improves Walking Performance across Stroke Walking Impairment Levels
Authors: Awosika Oluwole O. , Chan Dorothy , Sucharew Heidi J. , Boyne Pierce , Bhattacharya Amit , Dunning Kari , Kissela Brett M.
Published: January 2022

Background: Post-stroke walking impairment is a significant cause of chronic disability worldwide and often leads to loss of life roles for survivors and their caregivers. Walking impairment is traditionally classified into mild (>0.8 m/s), moderate (0.41–0.8 m/s), and severe (≤0.4 m/s), and those categorized as “severe” are more likely to be homebound and at greater risk of falls, fractures, and rehospitalization. In addition, there are minimal effective walking rehabilitation strategies currently available for this subgroup. Backward locomotor treadmill training (BLTT) is a novel and promising training approach that has been demonstrated to be safe and feasible across all levels of impairment; however, its benefits across baseline walking impairment levels (severe (≤0.4 m/s) vs. mild–moderate (>0.4 m/s)) have not been examined. Methods: Thirty-nine adults (>6 months post-stroke) underwent 6 days of BLTT (3×/week) over 2 weeks. Baseline and PRE to POST changes were measured during treadmill training and overground walking. Results: Individuals with baseline severe walking impairment were at a more significant functional disadvantage across all spatiotemporal walking measures at baseline and demonstrated fewer overall gains post-training. However, contrary to our working hypothesis, both groups experienced comparable increases in cadence, bilateral percent single support times, and step lengths. Conclusion: BLTT is well tolerated and beneficial across all walking impairment levels, and baseline walking speed (≤0.4 m/s) should serve as a covariate in the design of future walking rehabilitation trials.
Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction
Authors: Müller-Franzes Gustav , Nebelung Sven , Schock Justus , Haarburger Christoph , Khader Firas , Pedersoli Federico , Schulze-Hagen Maximilian , Kuhl Christiane , Truhn Daniel
Published: January 2022

Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were Cmean = 0.58 [most reliable] vs. Cmean = 0.56 [all] (p < 0.001, CT) and Cmean = 0.58 vs. Cmean = 0.57 (p = 0.23, MRI). Thus, preceding reliability analyses and selection of the most reliable radiomic features improves the underlying model’s ability to predict patient survival across clinical imaging modalities and tumor entities.
Robotic Herding of Farm Animals Using a Network of Barking Aerial Drones
Authors: Li Xiaohui , Huang Hailong , Savkin Andrey , Zhang Jian
Published: January 2022

This paper proposes a novel robotic animal herding system based on a network of autonomous barking drones. The objective of such a system is to replace traditional herding methods (e.g., dogs) so that a large number (e.g., thousands) of farm animals such as sheep can be quickly collected from a sparse status and then driven to a designated location (e.g., a sheepfold). In this paper, we particularly focus on the motion control of the barking drones. To this end, a computationally efficient sliding mode based control algorithm is developed, which navigates the drones to track the moving boundary of the animals’ footprint and enables the drones to avoid collisions with others. Extensive computer simulations, where the dynamics of the animals follow Reynolds’ rules, show the effectiveness of the proposed approach.
Valorization of Fruit Peels into Biovanillin and Statistical Optimization of Process Using Enterobacter hormaechei through Solid-State Fermentation
Authors: Mehmood Tahir , Ahmed Sibtain , Waseem Rida , Saeed Shagufta , Ahmed Waqas , Irfan Muhammad , Ullah Azmat
Published: January 2022

Vanillin is a secondary metabolite of plants and the major organoleptic aroma component of natural vanilla. Nowadays, the chemical synthesis method used for vanillin production has been rejected by the United States and European legislation, while plant-derived vanillin is expensive. The current study demonstrates vanillin production via solid-state fermentation (SSF) by Enterobacter hormaechei using different ferulic acid-rich fruit peels as substrates. From different ferulic acid-rich fruit peels (pomegranate, banana, and orange) screened Punica granatum (pomegranate) peels yielded maximum biovanillin (0.09 mg/g) after 24 h. Different bioprocess parameters, including moisture content, inoculum size, pH, and temperature, were optimized using central composite design (CCD) of the response surface methodology (RSM). The maximum biovanillin yield (0.462 mg/g) from Punica granatum peels was achieved at 60% moisture content, 2 mL inoculum size, 6.5 pH, and 32 °C temperature. An F-value of 12.94 and a p-value of 0.00 were recorded by the variance analysis indicated the proposed model’s significance. The coefficient of determination (R2) confirmed the model’s goodness of fit, having a value of 91.89%, which indicated the model’s accuracy. The optimally produced biovanillin was extracted and confirmed using FTIR. Further purity analysis was done by HPLC and the biovanillin was reported to be 99.2% pure. The results demonstrated that microbial conversion of ferulic acid-rich fruit peels to biovanillin offers a cost-effective approach for the industrial production of biovanillin.
Capillary Sodium Dodecyl Sulfate Agarose Gel Electrophoresis of Proteins
Authors: Sarkozy Daniel , Guttman Andras
Published: January 2022

Capillary sodium dodecyl sulfate gel electrophoresis has long been used for the analysis of proteins, mostly either with entangled polymer networks or translationally cross-linked gels. In this paper capillary agarose gel electrophoresis is introduced for the separation of low molecular weight immunoglobulin subunits. The light (LC~24 kDa) and heavy (HC~50 kDa) chain fragments of a monoclonal antibody therapeutic drug were used to optimize the sieving matrix composition of the agarose/Tris-borate-EDTA (TBE) systems. The agarose and boric acid contents were systematically varied between 0.2–1.0% and 320–640 mM, respectively. The influence of several physical parameters such as viscosity and electroosmotic flow were also investigated, the latter to shed light on its effect on the electrokinetic injection bias. Three dimensional Ferguson plots were utilized to better understand the sieving performance of the various agarose/TBE ratio gels, especially relying on their slope (retardation coefficient, KR) value differences. The best resolution between the LC and non-glycosylated HC IgG subunits was obtained by utilizing the molecular sieving effect of the 1% agarose/320 mM boric acid composition (ΔKR = 0.035). On the other hand, the 0.8% agarose/640 mM boric acid gel showed the highest separation power between the similar molecular weight, but different surface charge density non-glycosylated HC and HC fragments (ΔKR = 0.005). It is important to note that the agarose-based gel-buffer systems did not require any capillary regeneration steps between runs other than simple replenishment of the sieving matrix, significantly speeding up analysis cycle time.
Ventilation Performance Evaluation of a Negative-Pressurized Isolation Room for Emergency Departments
Authors: Wang Fujen , Permana Indra , Chaerasari Citra , Lee Kwowhei , Chang Tongbou , Rakshit Dibakar
Published: January 2022

Due to the emergence of COVID-19 becoming a significant pandemic worldwide, hospitals are expected to be capable and flexible in responding to the pandemic situation. Moreover, as frontline healthcare staff, emergency department (ED) staff have a high possibility of exposure risk to infectious airborne. The ED isolation room will possibly and effectively isolate the infected patient, therefore safekeeping frontline healthcare staff and controlling the outbreak. However, there is still limited knowledge available regarding isolation room facilities specifically for the emergency department. In this study, field measurement is conducted in an ED isolation room located in Taiwan. CFD simulation is employed to simulate and investigate the airflow and airborne contaminant distribution. Instead of high air-change rates (ACH) that purposes for dilution, this study proposes the arrangement of exhaust air grilles to improve the contaminant removal. The results reveal that the exhaust air grille placed behind the patient’s head is optimized to dilute airborne contaminants.
Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm
Authors: Gómez-Losada Álvaro , Asencio-Cortés Gualberto , Duch-Brown Néstor
Published: January 2022

Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.
Separation of Multi-Material Polymer Combinations Produced by Joining Using Pin-like Structures
Authors: Wolf Michael , Drummer Dietmar
Published: January 2022

In industrial applications, multi-material joints are becoming increasingly important to achieve a sustainable and resource-saving production. Not only high mechanical properties during the component use have to be given, but also possibilities to separate the joint after end of life are crucial. The recycling and re-use of the materials plays an increasing role in the process chain. Conventional multi-material joints can be separated by cutting out the joining zone, solvents, or thermal degradation. However, these methods result in a loss of material, damage to the base material, or high energy consumption. Therefore, novel joining methods are desirable, such as the joining using pin-like structures. The potential of this novel method for joining adhesion incompatible materials has been demonstrated in previous studies. This paper studies the separability of these connections. Therefore, joints between polyamide 66 (PA66) and polypropylene (PP) as well as PA66 and polymethylmethacrylate (PMMA) are investigated by means of thermal separation and shredding with subsequent sorting using the density difference of the materials. The separated components were investigated by analytical methods (including dissolution tests, viscosity number analysis, and Fourier-transform infrared spectroscopy) with respect to varietal purity and possible degradation effects. It could be shown that shredding allows a complete separation of the multi-material joint into its individual components without material residues or material loss. For thermal separation, material residues of PP or PMMA could be detected in the pin gaps of the PA66. For both separation methods, an influence on the base materials due to degradation effects could be excluded. It can be stated that joining using pin-like structures in vibration welding technology offers a sustainable production of multi-material joints with high recyclability.
COVID-19-Associated Pulmonary Aspergillosis in a Tertiary Hospital
Authors: Marta García-Clemente , Lorena Forcelledo-Espina , Laura Martínez-Vega , Angela Lanza-Martínez , Blanca Leoz-Gordillo , Rodrigo Albillos-Almaraz , Marta Solís-García , Santiago Melón-García , Liliana Pérez-Martínez , Maria Luisa Sánchez-Nuñez , de la Rasilla Teresa Peláez-García
Published: January 2022

Our study aims to assess the prevalence of CAPA (COVID-19-associated pulmonary aspergillosis) and describe the associated risk factors and their impact on mortality. A prospective study was conducted. We included patients with COVID-19 disease who were admitted to the ICU with a diagnosis of respiratory failur. Mycological culture and other biomarkers (calcofluor staining, LFD, LFA, PCR, GM, and B-D-glucan) were performed. A total of 300 patients were included in the study. Thirty-five patients were diagnosed with CAPA (prevalence 11.7%). During admission, 57 patients died (19%), and, in the group of CAPA patients, mortality was 31.4%. In multivariate analysis, independent risk factors associated with CAPA diagnosis were age (OR: 1.05; 95% CI 1.01–1.09; p = 0.037), chronic lung disease (OR: 3.85; 95% CI 1.02–14.9; p = 0.049) and treatment with tocilizumab during admission (OR: 14.5; 95% 6.1–34.9; p = 0.001). Factors independently associated with mortality were age (OR: 1.06; 95% CI 1.01–1.11; p = 0.014) and CAPA diagnosis during admission (OR: 3.34; 95% CI 1.38–8.08; p = 0.007). CAPA is an infection that appears in many patients with COVID-19 disease. CAPA is associated with high mortality rates, which may be reduced by early diagnosis and initiation of appropriate antifungal therapy, so screening of COVID-19 ARDS (acute respiratory distress syndrome) patients for CAPA is essential.
Function Replacement Decision-Making for Parking Space Renewal Based on Association Rules Mining
Authors: Xia Bing , Ruan Yichen
Published: January 2022

Parking lots are typical urban spaces with a large total area and scattered distribution. With the development of smart cars and shared driving, parking demand is likely to decline. Thus, the reuse of existing parking spaces presents important opportunities and challenges in the process of the digital transformation of future cities. One of the key issues in the sustainable renewal of parking spaces is to make scientific decisions regarding the replacement of functions. Based on relevant data from the urban area of Hangzhou, this study analyzes the spatial co-location relationships between parking spaces and other urban points of interest (POIs). By mining the function association patterns, this research aims to establish a decision-making support model for the function replacement of parking spaces. The following conclusions are drawn: (1) based on charge, size, and affiliation, parking lots can be divided into eight categories; (2) parking lots of different charges, sizes, and affiliations differ in their spatial co-location relationships with POIs; and (3) most parking lots are suitable for catering services, followed by companies and commercial residences. The innovations of this research lie in providing scientific references for the renewal of urban fragmented spaces by mining urban function association rules at the microscale.
Path Planning Strategy for a Manipulator Based on a Heuristically Constructed Network
Authors: Fei Junting , Chen Gang , Jia Qingxuan , Liang Changchun , Wang Ruiquan
Published: January 2022

Collision-free path planning of manipulators is becoming indispensable for space exploration and on-orbit operation. Manipulators in these scenarios are restrained in terms of computing resources and storage, so the path planning method used in such tasks is usually limited in its operating time and the amount of data transmission. In this paper, a heuristically constructed network (HCN) construction strategy is proposed. The HCN construction contains three steps: determining the number of hub configurations and selecting and connecting hub configurations. Considering the connection time and connectivity of HCN, the number of hub configurations is determined first. The selection of hub configurations includes the division of work space and the optimization of the hub configurations. The work space can be divided by considering comprehensively the similarity among the various configurations within the same region, the dissimilarity among all regions, and the correlation among adjacent regions. The hub configurations can be selected by establishing and solving the optimization model. Finally, these hub configurations are connected to obtain the HCN. The simulation indicates that the path points number and the planning time is decreased by 45.5% and 48.4%, respectively, which verify the correctness and effectiveness of the proposed path planning strategy based on the HCN.
Geant4 Simulation of Precipitated Activity-to-γ-Dose Rate Conversion Factors for Radon and Thoron Decay Products
Authors: Yakovleva Valentina , Yakovlev Grigorii , Parovik Roman , Smirnov Sergei , Kobzev Aleksey
Published: January 2022

The results of modeling the conversion factor from rainfall-deposited unit activity of gamma-emitting radon and thoron daughter decay products to their created gamma-radiation dose rate as a function of height above the Earth’s surface using the Geant4 toolkit are presented in this paper. Thin layers of water, soil, and air, with the height of 0.1–10 mm, are considered as the source in order to examine whether the composition of the radiation source environment affects the simulation result. Cases with different absorber-atmosphere densities are simulated. The contribution of each radionuclide 212Bi, 214Bi, 212Pb, 214Pb and 208Tl to the total gamma background was determined. The dependence of dose rate growth during the precipitation period on the detector position in relation to the area covered by precipitation was investigated numerically. The obtained conversion factors are universal values, because do not depend on soil type (material) on which radionuclides are deposited by precipitation. These coefficients can be used for solving both direct tasks of radiation background recovery during precipitation and inverse tasks of determining the intensity and amount of precipitation by the known gamma background, as well as tasks to decipher the gamma background by the shape of the response to various phenomena. Also in this work, it is shown how thoron decay products can affect the response shape of gamma background on atmospheric precipitation.
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration
Authors: Pereira Jorge , Mendes Jérôme , Júnior Jorge S. S. , Viegas Carlos , Paulo João Ruivo
Published: January 2022

Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.
New Concepts of Vertex Covering in Cubic Graphs with Its Applications
Authors: Jiang Huiqin , Talebi Ali Asghar , Shao Zehui , Sadati Seyed Hossein , Rashmanlou Hossein
Published: January 2022

Graphs serve as one of the main tools for the mathematical modeling of various human problems. Fuzzy graphs have the ability to solve uncertain and ambiguous problems. The cubic graph, which has recently gained a position in the fuzzy graph family, has shown good capabilities when faced with problems that cannot be expressed by fuzzy graphs and interval-valued fuzzy graphs. Simultaneous application of fuzzy and interval-valued fuzzy membership indicates a high flexibility in modeling uncertainty issues. The vertex cover is a fundamental issue in graph theory that has wide application in the real world. The previous definition limitations in the vertex covering of fuzzy graphs has directed us to offer new classifications in terms of cubic graph. In this study, we introduced the strong vertex covering and independent vertex covering in a cubic graph with strong edges and described some of its properties. One of the motives of this research was to examine the changes in the strong vertex covering number of a cubic graph if one vertex is omitted. This issue can play a decisive role in covering the graph vertices. Since many of the problems ahead are of hybrid type, by reviewing some operations on the cubic graph we were able to determine the strong vertex covering number on the most important cubic product operations. Finally, two applications of strong vertex covering and strong vertex independence are presented.
Geometric Brownian Motion (GBM) of Stock Indexes and Financial Market Uncertainty in the Context of Non-Crisis and Financial Crisis Scenarios
Authors: Brătian Vasile , Acu Ana-Maria , Mihaiu Diana Marieta , Șerban Radu-Alexandru
Published: January 2022

The present article proposes a methodology for modeling the evolution of stock market indexes for 2020 using geometric Brownian motion (GBM), but in which drift and diffusion are determined considering two states of economic conjunctures (states of the economy), i.e., non-crisis and financial crisis. Based on this approach, we have found that the GBM proved to be a suitable model for making forecasts of stock market index values, as it describes quite well their future evolution. However, the model proposed by us, modified geometric Brownian motion (mGBM), brings some contributions that better describe the future evolution of stock indexes. Evidence in this regard was provided by analyzing the DAX, S&P 500, and SHANGHAI Composite stock indexes. Throughout the research, it was also found that the entropy of these markets, analyzed in the periods of non-crisis and financial crisis, does not differ significantly for DAX—German Stock Exchange (EU) and S&P 500—New York Stock Exchange (US), and insignificant differences for SHANGHAI Composite—Shanghai Stock Exchange (Asia). Given the fact that there is a direct link between market efficiency and their entropy (high entropy—high efficiency; low entropy—low efficiency), it can be deduced that the analyzed markets are information-efficient in both economic conjunctures, and, in this case, the use of GBM for forecasting is justified, as the prices have a random evolution (random walk).
Improving Machine Reading Comprehension with Multi-Task Learning and Self-Training
Authors: Ouyang Jianquan , Fu Mengen
Published: January 2022

Machine Reading Comprehension (MRC) is an AI challenge that requires machines to determine the correct answer to a question based on a given passage, in which extractive MRC requires extracting an answer span to a question from a given passage, such as the task of span extraction. In contrast, non-extractive MRC infers answers from the content of reference passages, including Yes/No question answering to unanswerable questions. Due to the specificity of the two types of MRC tasks, researchers usually work on one type of task separately, but real-life application situations often require models that can handle many different types of tasks in parallel. Therefore, to meet the comprehensive requirements in such application situations, we construct a multi-task fusion training reading comprehension model based on the BERT pre-training model. The model uses the BERT pre-training model to obtain contextual representations, which is then shared by three downstream sub-modules for span extraction, Yes/No question answering, and unanswerable questions, next we fuse the outputs of the three sub-modules into a new span extraction output and use the fused cross-entropy loss function for global training. In the training phase, since our model requires a large amount of labeled training data, which is often expensive to obtain or unavailable in many tasks, we additionally use self-training to generate pseudo-labeled training data to train our model to improve its accuracy and generalization performance. We evaluated the SQuAD2.0 and CAIL2019 datasets. The experiments show that our model can efficiently handle different tasks. We achieved 83.2EM and 86.7F1 scores on the SQuAD2.0 dataset and 73.0EM and 85.3F1 scores on the CAIL2019 dataset.