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Abstract Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.

Plant triterpenoids represent a diverse group of secondary metabolites and are thought to be valuable for therapeutic applications. For drug development, lead optimization, better knowledge of biological pathways, and high-throughput detection of secondary metabolites in plant extracts are crucial. This paper describes a qualitative method for the rapid and accurate identification of various triterpenoids in plant extracts using the LC-HR-ESI-MS/MS tool in combination with the data-dependent acquisition (DD) approach. A total of 44 isolated, purified, and characterized triterpenoids were analyzed. HR-MS spectra and tandem mass spectra (MS/MS) of each compound were recorded in the positive ionization mode in two different sets of collisional energies, i.e., (25–62.5 eV), and fixed collisional energies (10, 20, 30, and 40 eV). As a result, three triterpenoids were identified in all plant extracts using the retention time, high-resolution mass spectra, and/or MS/MS spectra. The developed method will be helpful with other plant extracts/botanicals, as well as in the search for new triterpenoids in the kingdom Plantae.

Abstract In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space such that a hyperplane can be used as the optimal decision surface. Instead of maximizing the likelihood of target labels for individual samples, our loss function pushes the network to produce feature distributions yielding high inter-class separation and low intra-class separation. The mean values of the learned distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy. Moreover, GCCS provides improved robustness against adversarial perturbations, outperforming models trained with conventional adversarial training (AT). In particular, our model learns a decision space that minimizes the presence of short paths toward neighboring decision regions. We provide a comprehensive empirical evaluation that shows how GCCS outperforms state-of-the-art approaches over challenging datasets for targeted and untargeted gradient-based, as well as gradient-free adversarial attacks, both in terms of classification accuracy and adversarial robustness.

Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on the Twitter dataset and the robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively.

Metabolomics has been used as a powerful tool for the analysis, and drug-lead identification in medicinal plants and herbal medicines. For the coverage of a broader range of plant-based metabolites using LC-MS, one of the important parameters is the selection of analysis mode and data processing for identification. This paper describes the utility of two distinctive acquisition modes in combination, a classic data-dependent acquisition (DDA) mode and a data-independent acquisition (DIA) mode for obtaining the mass spectrometry data of plant extracts using LC-ESI-QTOF/MS. Tribulus terrestris plant was used as a case study. We have applied three-step data analysis pipeline 1-annotation and putative identification of metabolites, 2-validation, and relative quantification, and 3-multivariate analysis using open-access MS-DIAL, Skyline, and Perseus software. A total of four samples of T. terrestris (aqueous extracts), two fruits, and two whole plant samples, from two different regions, were used. By combining data analysis results of plant fruit samples from two different regions, a total of 95 and 77 metabolites were identified in positive and negative ionization modes, respectively. Similarly, in the analysis of the whole plant from two different regions, 75 and 76 metabolites were identified in positive and negative ionization modes, respectively. We suggested the use of DDA mode for annotation, identification of metabolites, and generation of transition lists in MS-DIAL, furthermore, the use of DIA acquisition mode for enhancing metabolites sensitivity in complex samples, deconvolute MS1/MS2 spectra in Skyline for the quantitative performance and analytical reliability. The developed protocol can be used for the broader coverage of plant-based metabolites.

Asthma is a major non-communicable disease (NCD) affecting adults and children, among whom it is the most common chronic disease reported all around the globe1 Asthma has been a significant cause of hospitalizations and financial burdens for low and lower- middle -income countries like Pakistan. According to the latest WHO data published in 2020, asthma deaths in Pakistan reached a staggering 20,750 or 1.42% of total deaths.2 Many etiologies including social and environmental factors, genetic predispositions, and ambient air pollution, influence asthma morbidity and mortality. In particular, the worsening air quality index in the country needs to be a point of focus as a significant cause of asthma related morbidity. The rapid decline in air quality levels in Pakistan, particularly in major industrial cities such as Lahore and Karachi, is a cause of grave concern. For example, air quality in Lahore has declined over the last two decades: in 1998, its pollution concentration was 33 µg/m³. By 2016, it had doubled to 64 µg/m³—more than six times the WHO guideline.3 Similarly, Karachi, the largest city in Pakistan, has a PM2.5 concentrations, which is 3.4 times the WHO annual air quality guideline value, an alarming scenario for a city where environmental policies and infrastructure is not in place.4 A study in Lahore noted the role of ambient air pollution among various population groups of the city. The research showed a positive Pearson correlation between environmental air pollutants and the number of asthma patients visiting the OPD.5 However, a similar, comprehensive study has not been conducted in Karachi on the same level. As in most cases, the first step toward solving a problem is to recognize it. Hence there’s a need for more research to be conducted to determine the number of hospitalizations of asthmatic patients caused by low air quality as it will give us a better understanding of the true extent of the issue at hand and allow more appropriate prioritization, in the context of public health by the government. Once this is done, resources can be allocated more efficiently towards areas and populations suffering more acutely to relieve the suffering of the majority. In addition, educational campaigns can be designed considering the population’s educational demographics and general knowledge regarding the exact reasons for the worsening air quality in those areas. ---Continue

Due to the numerous real-world applications of the crowd counting job, it has become a popular research topic. Modern crowd counting systems have a sophisticated structure and employ a filter on a big image size, making them difficult to use. Because these technologies are computationally intensive and difficult to implement in small surveillance systems, they are not appropriate for use in small surveillance systems. They also function poorly in a variety of sizes and densities, as well. Transfer learning and deep convolutional neural network architecture are used to create a modest but efficient network, which we describe herein. We named the proposed crowd counting architecture deep crowd transfer network (DCTNets) since it incorporates both deep learning and transfer learning principles into a single system. Among DCTNets’ key components are a detection module that is based on mask R-CNNs and an estimate module that is based on deep convolutional neural networks. In the first step, we apply transfer learning to the Mask R-CNN model using the datasets ShanghaiTech, JHU-CROWD++, and UCF-QNRF. After that, we train and evaluate the complete architecture on these datasets using the transfer learning results. Input images are sent through a Mask R-CNN, which counts individuals and segments the counted region, then through an estimation network, which estimates the population size, and finally through a merge of the outputs from the two models. According to the findings of comparative tests, the proposed model outperforms existing state-of-the-art approaches on the ShanghaiTech, JHU-CROWD++, and UCF-QNRF datasets.

Abstract A potent napin protein has been thoroughly characterized from seeds of rocket salad (Eruca sativa). Eruca sativa napin (EsNap) was purified by ammonium sulfate precipitation (70%) and size-exclusion chromatography. Single intact 16 kDa EsNap band was reduced to 11 and 5 kDa bands respectively on SDS-PAGE. Nano LC–MS/MS yielded two fragments comprising of 26 residues which showed 100% sequence identity with napin-3 of Brassica napus. CD spectroscopy indicated a dominant α-helical structure of EsNap. Monodispersity of EsNap was verified by dynamic light scattering, which also confirmed the monomeric status with a corresponding hydrodynamic radius of 2.4 ± 0.2 nm. An elongated ab initio shape of EsNap was calculated based on SAXS data, with an Rg of 1.96 ± 0.1 nm. The ab initio model calculated by DAMMIF with P1 symmetry and a volume of approx. 31,100 nm3, which corresponded to a molecular weight of approximately 15.5 kDa. The comparison of the SAXS and ab initio modeling showed a minimized χ2-value of 1.87, confirming a similar molecular structure. A homology model was predicted using the coordinate information of Brassica napus rproBnIb (PDB ID: 1SM7). EsNap exhibited strong antifungal activity by significantly inhibiting the growth of Fusarium graminearum. EsNap also showed cytotoxicity against the hepatic cell line Huh7 and the obtained IC50 value was 20.49 µM. Further, strong entomotoxic activity was experienced against different life stages of stored grain insect pest T. castaneum. The result of this study shows insights that can be used in developing potential antifungal, anti-cancerous and insect resistance agents in the future using EsNap from E. sativa.

Several applications of artificially modeled drivers, such as autonomous vehicles (AVs) or surrounding traffic in driving simulations, aim to provide not only functional but also human-like behavior. The development of human-like AVs is expected to improve the interaction between AVs and humans in traffic, whereas, in a driving simulation, the objective is to create realistic replicas of real driving scenarios to investigate various research questions under safe and reproducible conditions. In urban traffic, driving behavior strongly depends on the situational context, which introduces new challenges not only for modeling but also for the evaluation of such models. However, current objective assessment strategies rarely consider situational context and human similarity, whereas subjective approaches are not suitable for iterative development processes. In this paper, we present a first attempt to make the plausibility and human-likeness of vehicles’ trajectories objectively measurable. A multidimensional quality function is presented that incorporates various parameters characterizing human-like driving behavior and compares each of those parameters to human driving behavior under similar conditions. Among other things, our validation results show that the presented evaluation methodology is scalable to a wide range of situations has the ability to identify model weaknesses, and is able to reflect the way people distinguish between artificial and human behavior.

Externally variable magnetic field was incorporated with the combination of laser induced breakdown spectroscopy (LIBS) to enhance the emission characteristics of aluminum (Al) plasma. Significant emission enhancement of laser induced plasma (LIP) was obtained at different magnetic field strengths, for instance, enhancement factors of about 1.2, 1.3 and 1.4 times were observed at field-strength of 0.4, 0.5 and 0.6 T, respectively. The electron-impact excitation rates and recombination process were increased at higher field-strengths, which led to the higher emission signal due the stronger plasma confinement by the field. The electron number density and electron temperature were measured using the spectroscopic techniques at several delay times. At higher field strengths, both electron density and electron excitation temperature showed an increased trend as compared to the case when No-field was applied. Hence, the research has significance for enhancing the plasma signal which led to improve the LIBS sensitivity.

Abstract Background The transition to a biobased economy involving the depolymerization and fermentation of renewable agro-industrial sources is a challenge that can only be met by achieving the efficient hydrolysis of biomass to monosaccharides. In nature, lignocellulosic biomass is mainly decomposed by fungi. We recently identified six efficient cellulose degraders by screening fungi from Vietnam. Results We characterized a high-performance cellulase-producing strain, with an activity of 0.06 U/mg, which was identified as a member of the Fusarium solani species complex linkage 6 (Fusarium metavorans), isolated from mangrove wood (FW16.1, deposited as DSM105788). The genome, representing nine potential chromosomes, was sequenced using PacBio and Illumina technology. In-depth secretome analysis using six different synthetic and artificial cellulose substrates and two agro-industrial waste products identified 500 proteins, including 135 enzymes assigned to five different carbohydrate-active enzyme (CAZyme) classes. The F. metavorans enzyme cocktail was tested for saccharification activity on pre-treated sugarcane bagasse, as well as untreated sugarcane bagasse and maize leaves, where it was complemented with the commercial enzyme mixture Accellerase 1500. In the untreated sugarcane bagasse and maize leaves, initial cell wall degradation was observed in the presence of at least 196 µg/mL of the in-house cocktail. Increasing the dose to 336 µg/mL facilitated the saccharification of untreated sugarcane biomass, but had no further effect on the pre-treated biomass. Conclusion Our results show that F. metavorans DSM105788 is a promising alternative pre-treatment for the degradation of agro-industrial lignocellulosic materials. The enzyme cocktail promotes the debranching of biopolymers surrounding the cellulose fibers and releases reduced sugars without process disadvantages or loss of carbohydrates.

In this study, we have investigated the fragmentation of the widely used steroidal pharmaceutical drugs (n = 14), complexed by a singly charged proton or alkali metal ion (Li+, Na+, K+) using Ion trap and quadrupole time-of-flight mass spectrometers. Spectra were collected by LC-MS/MS analysis using system automated collision energy i.e., of 25–60 eV. Theoretical calculations were also calculated using DFT software. The metal complexes showed different fragmentation pathways not commonly observed for protonated compounds. There was a distinct difference observed in the relative intensities of some common fragments for free vs. metallated drugs. Some major fragments from protonated and lithium adducts showed close resemblance, while sodium and potassium adducts showed different fragments. Theoretical calculations showed a distinct difference in the position of attachment of proton and metals. This adducts ion fragmentation information will be helpful for the identification of these compounds in complex samples.

Drinking water is the main source of fluoride intake for the human body and its regulated consumption helps in decreasing dental caries. However, excessive fluoride consumption over a prolonged time period causes fluorosis disease which adversely affects many tissues and organs of the body. This paper describes the evaluation of chronic intoxication of fluoride on human serum metabolome. The untargeted metabolomics approach using UPLC-QTOF-MS/MS is applied for metabolomic profiling, whereas the estimation of fluoride in serum samples was carried out using the ion-selective electrode (ISE). Fluoride concentration was found to be 0.16–1.25 mg/L in serum samples of 39 fluorosis patients and 0.008–0.045 mg/L in 20 healthy samples. A total of 47 metabolites were identified based on the high-resolution mass spectrometry analysis. A volcano plot was generated to discriminate features that are significantly different between the fluorosis and healthy groups at the probability of 0.05 and fold change ≥ 2. Among all identified metabolites, intensities of ten differential identified metabolites including inosine, α-linolenic acid, guanosine, octanoyl-L-carnitine, His-Trp, phytosphingosine, lauroyl-L-carnitine, hydrocortisone, deoxyinosine and dodecanedioic acid have been found altered in disease samples compared to healthy controls. Major pathways identified based on these metabolites include energy metabolism, fatty acid oxidation, purine degradation pathway, elevated protein degradation, and increased ω-6 fatty acid linoleate signatures were observed.

Chicken meat is among the common and relatively inexpensive source of protein consumed worldwide from the poultry industry. Many communities show concern regarding the procedure of slaughtering animals for meat consumption due to ethical, religious, or cultural reasons. Liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) based untargeted metabolomics of 40 chicken meat samples were evaluated to differentiate meat samples based on slaughtering methods. Samples were grouped into, Zabiha (cutting neck without detaching spinal cord) and Non-Zabiha (completely detaching neck). A volcano plot reveals at least 150 features found significantly different between the two groups having ≥ 2-fold changes in intensities with p-values ≤ 0.05. Among them 05 identified and 25 unidentified metabolites have clear differences in peak intensities. The identified features can be employed to differentiate meat obtained from different slaughtering methods. A characteristic pattern based on principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) was observed among the groups. The results will benefit Halal certification, food safety, and security agencies to curb food fraud.

Dereplication of crude plant extracts through liquid chromatography-mass spectrometry is a powerful technique for the discovery of novel natural products. Unfortunately, this technique is often plagued by a low level of confidence in natural product identification. This is mainly due to the lack of extensive chromatographic and mass spectrometric optimizations that result in improper and incomplete MS/MS fragmentation data. This study proposes a solution to this problem by the optimization of chromatographic separation and mass spectrometry parameters. We report herein a direct and high-throughput strategy for natural product dereplication in five Salvia species using high-resolution ESI-QTOF-MS/MS data. In the present study, we were able to identify a total of forty-seven natural products in crude extracts of five Salvia species using MS/MS fragmentation data. In addition to dereplication of Salvia species, quantitative profiling of twenty-one bioactive constituents of the genus was also performed on an ion trap mass spectrometer. For the quantitation study, method development focused on chromatographic optimizations to achieve maximum sensitivity. The developed dereplication and quantitation strategy can be extended to develop comprehensive metabolic profiles of other plant genera and species and thus can prove useful in the field of drug discovery from plants.

Natural product screening in plants has always been a difficult task due to the complex nature of the plant material and diverse structures of the compounds present in them. Flavonoids are important and diverse class of plant secondary metabolites with numerous medicinal activities. The present study focuses on the development of a high-resolution tandem mass spectral library for the rapid and authentic identification of common flavonoids. A total of forty flavonoid standards belong to class flavones, isoflavones, flavanones, flavanols and anthocyanins were pooled into two solutions applying logP-based strategy. The flavonoids were analyzed using LC-QTOF-MS high-resolution mass spectrometer with optimization of different instrumental parameters to achieve good sensitivity. The library was built by incorporating names, molecular formulae, exact masses, and MS, and MS/MS spectra of analyzed flavonoids using Bruker Library Editor tool. The fragmentation pattern observed for the standard compounds were compared to the fragments reported in the literature. To assess the practical implications, an extract of tea sample was analyzed and screened using the developed library, which resulted in the identification of three common flavonoids based on their HR-ESI-MS/MS spectral features. The established LC-HR-MS/MS method can be used for the targeted identification of flavonoids in complex samples like food material from different botanical families.

The rapid melting of glaciers has led to severe glacial-hydrological hazards in the Himalayas. An extreme example occurred on 7 February 2021, when a catastrophic mass flow descended from the Ronti glacier at Chamoli, Indian Himalaya, causing widespread devastation, with more than 200 people killed or missing, as well as severe damage to four hydropower projects. To disclose what happened to the Ronti glacier over the past several decades, here, we focused on glacier changes in the Dhauliganga catchment in Uttarakhand, India, over the past two decades. Another five glaciers in the catchment were also studied to map the regional detailed glacier changes. Our achievements are summarized as follows. (1) Based on Landsat images, we constructed two glacier inventories for the catchment in 2001 and 2020. We mapped nearly 413 debris-free glaciers in the catchment between 2001 and 2020 and analyzed the glacier area change at basin and altitude levels. (2) Debris-free glacier area decreased from 477.48 ± 35.23 km2 in 2001 to 418.52 ± 36.18 km2 in 2020, with a reduction of 58.95 km2 or 12.35% over the past two decades. (3) The geodetic mass balance was −0.27± 0.10 m w.e.a−1, with a glacier mass change of −0.12 Gt. a−1 from 2000 to 2013. Based on the surface elevation difference between the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) footprints (acquired from 2018 to 2021) and the National Aeronautics and Space Administration (NASA) DEM from 2000 to 2021, the average glacier geodetic mass balance was −0.22 ± 0.005 m w.e.a−1, and glacier mass change was −0.10 Gt a−1. (4) Our results were cross verified by available published elevation difference datasets covering multiple temporal periods, where mass balance was by −0.22 ± 0.002 m w.e.a−1 from 1975 to 2000 and −0.28 ± 0.0001 w.e.a−1 from 2000 to 2020. (5) Glacier 1 and Glacier 2, the two largest glaciers in the catchment, experienced a decreasing melt rate from 2000 to 2020, while Glacier 3, Glacier 4, and Glacier 5 demonstrated an increasing melt rate. However, Glacier 6, also known as the collapsed Ronti glacier, had a negative mass balance of −0.04 m w.e.a−1 from 2000 to 2005 and turned positive from 2005 onward with 0.06 m w.e.a−1 from 2005 to 2010, 0.19 m w.e.a−1 from 2010 to 2015, and 0.32 m w.e.a−1 from 2015 to 2020. We postulate that the Ronti glacier collapsed solely because of the significant mass accumulation observed between 3700 to 5500 m a.s.l. Our study helps to understand the collapsed glacier’s mass changes over the past two decades and highlights the necessity to monitor mass-gaining glaciers from space to forecast the risks of disasters.

Objectives: Food animals have been recognised as an important reservoir of extended-spectrum β-lactamase-producing Escherichia coli (ESBL-Ec). This study reports the draft genome sequence of a bovine ESBL-Ec from Pakistan. Methods: Genomic DNA from a bovine E. coli (PK-1421) was extracted and whole-genome sequencing was performed using an Illumina HiSeq 2500 platform. De novo genome assembly was performed using SPAdes v.3.9 and annotation of the assembled genome was performed using the DDBJ Fast Annotation and Submission Tool (DFAST). In silico tools from the Center for Genomic Epidemiology were used to find out for analysis of antimicrobial resistance genes, sequence type (ST), plasmid replicon type, serotype and phylogenetics. Results: Escherichia coli strain PK-1421 showed multiple drug resistance. The draft genome was calculated at 4 869 610 bp, comprising a total of 131 contigs with a G + C content of 50.6% and 4487 protein-coding sequences. Escherichia coli PK-1412 belongs to ST58 and serotype O75:H9. A large number of antimicrobial resistance genes associated with resistance to β-lactams (blaCTX-M-15, blaTEM-1B), trimethoprim (dfrA14), aminoglycosides [aph(3ʹ)-lb, aph(6)-ld], sulfonamides (sul2), tetracycline (tetA), macrolides (mdfA) and quinolones (qnrS1) were detected. A conjugation experiment showed successful transfer of the blaCTX-M-15 gene to an E. coli J53 recipient strain. The phylogenetic tree indicated that ST58 E. coli PK-1421 was distant from other strains. Conclusion: This is the first draft genome of an E. coli of bovine origin in Pakistan. The genome sequence presented here may be helpful to understand the genomic diversity and antimicrobial resistance potential of E. coli from large ruminants.

We present AuthNet: a novel framework for generic biometric authentication which, by learning a regularized mapping instead of a classification boundary, leads to higher performance and improved robustness. The biometric traits are mapped onto a latent space in which authorized and unauthorized users follow simple and well-behaved distributions. In turn, this enables simple and tunable decision boundaries to be employed in order to make a decision. We show that, differently from the deep learning and traditional template-based authentication systems, regularizing the latent space to simple target distributions leads to improved performance as measured in terms of Equal Error Rate (EER), accuracy, False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). Extensive experiments on publicly available datasets of faces and fingerprints confirm the superiority of AuthNet over existing methods.

This paper describes eight new triterpenoid saponins, including afzeliioside A (1), four acetylated afzeliiosides as pairs of inseparable regioisomers, called afzeliiosides B/C (2/3) and D/E (4/5), afzeliiosides F-H (6–8), and a known impatiprin C (9), which were isolated from the n-BuOH fraction of the liana of Microglossa afzelii. Their structures were established mainly by extensive spectroscopic analysis, including 1D and 2D NMR, HRFAB-MS, tandem ESI-MS/MS, and chemical methods, as well as a comparison of their spectral data with those of related compounds. All the isolates were screened for their cytotoxic activity against the CAL-27 oral squamous carcinoma cell line. Only compounds 4/5 (EC50 = 36.0 μg/mL (32.7 μM)) exhibited moderate cytotoxic activity. This work presents the first chemical and biological investigation of Microglossa afzelii and reports, for the first time, on the isolation of saponins in the genus Microglossa.

Ride hailing (RH) services have become a common mode of transportation in the last decade. Usually, statistical tools are used to improve their performance, whereby the tools typically divide the whole operational area into multiple regions. These tools usually assume that the regions are independent of each other even though vehicles from one region can be used to serve neighboring regions, thereby a method is required that consistently relates vehicle demand and supply between geographically neighboring regions or to the whole operating area. This hinders tapping into the full potential of region-based performance improvement techniques like the repositioning of idle vehicles. Therefore, we develop an innovative reachability function-based method that coherently builds a relation among all regions in the form of a spatial density of the measured quantity. We use it to calculate the differences of vehicle supply and demand for the whole operational area in the form of an imbalance density. Based on this, we derive a novel repositioning formulation that significantly reduces both the overall vehicle imbalances and the total distance of repositioning trips, and thus improves the long-term RH performance. We test the approach in an agent-based simulation for an RH scenario with automated vehicles, using open-source New York City taxi data as demand. The approach shows a remarkable improvement over the state of the art repositioning strategies that balance the fleet over the individual regions. Furthermore, we introduce kernel-based key performance indicators (KPIs) that can be calculated at the time of making repositioning decisions. We also show the correlation of the KPIs with long-term performance gains. We expect that these KPIs can benefit future statistical (machine learning) approaches for repositioning.

Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.

Hepatocellular carcinoma (HCC) is a typical cancer that has region specified analysis with the incidence of hepatitis C virus (HCV) infection. This study was conducted to improve the understanding of metabolic alterations associated with HCV induced HCC which can open up new strategies to monitor the high risk of HCC. Samples of the subjects with HCV, HCV induced chronic liver disease (CLD), HCV induced HCC, and healthy controls (HS) were collected after complete blood count (CBC), hepatitis viral load, α-fetoprotein (AFP), liver function tests, and albumin. A total of 147 serum samples including HCC (n = 11), CLD (n = 24), HCV (n = 71), and HS (n = 41) were analyzed by LC-ESI-MS/MS. The 21 compounds were found to be responsible for group discrimination after the application of chemometric tools. N-fructosyl tyrosine and hydroxyindoleacetic acid showed an increase in level whereas L-aspartyl-L-phenylalanine and thyroxine showed a consistent decrease in the progression of HCV to HCC in comparison with HS indicating their importance for early detection. The biological pathways such as glycerophospholipid metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism and tryptophan metabolism showed alteration in some metabolites. The method was internally validated by ROC plot showing AUC value for HS, HCV, CLD, and HCC as 0.99, 1, 1, and 0.89, respectively; while 16 blind samples were also validated with 93% specificity. The untargeted metabolomics investigation of HCV, CLD, and HCC can help to understand the progression of HCV-induced HCC. It reveals significant differences in metabolites to predict prognostic and diagnostic markers.

Aquilegia fragrans (AF) and Aquilegia pubiflora (AP) are the two medicinally important species of genus Aquilegia used for the treatment of various diseases and infections. This paper describes the potential of fetal hemoglobin induction activity of the methanolic extracts of AF and AP in K562 cell line. AF and AP have shown 27.147 ± 1.376 and 32.786 ± 1.048 percent erythroid induction, respectively at 15.625 (µg/mL) concentration which suggested that both plants can be the source of potential fetal hemoglobin inducers and may be used for the treatment of β-thalassemia. Phytochemical analyses of both species were also evaluated by using high-resolution LC-ESI-QTOF-MS/MS techniques. A Total of thirty compounds were identified using positive and negative ionization modes. The identification was based on the matching of high-resolution masses, isotopic pattern, and MS/MS fragmentation. Several statistical analyses were performed to evaluate the distribution of compounds in both species. Identified compounds belong to various classes including flavonoids, steroids, lignans, terpenoids, benzofuran and coumarins. The established chemical fingerprints will be helpful in standardization and quality control of plant extracts.

The discovery and identification of novel natural products of medicinal importance in the herbal medicine industry becomes a challenge. The complexity of this process can be reduced by dereplication strategies. The current study includes a method based on high-performance liquid chromatography (HPLC), using the evaporative light scattering detector (ELSD) to identify the 12 most common secondary metabolites in plant extracts. Twelve compounds including rutin, taxifolin, quercetin, apigenin, kaempferol, betulinic acid, oleanolic acid, betulin, lupeol, stigmasterol, and β-sitosterol were analyzed simultaneously. The polarity of the compounds varied greatly from highly polar (flavonoids) to non-polar (triterpenes and sterols). This method was also tested for HPLC-DAD and HPLC-ESI-MS/MS analysis. Oleanolic acid and ursolic acid could not be separated in HPLC-ELSD analysis but were differentiated using LC-ESI-MS/MS analysis due to different fragment ions. The regression values (R2 > 0.996) showed good linearity in the range of 50–1000 µg/mL for all compounds. The range of LOD and LOQ values were 7.76–38.30 µg/mL and 23.52–116.06 µg/mL, respectively. %RSD and % trueness values of inter and intraday studies were mostly

Staphylotrichum longicolleum FW57 (DSM105789) is a prolific chitinolytic fungus isolated from wood, with a chitinase activity of 0.11 ± 0.01 U/mg. We selected this strain for genome sequencing and annotation, and compiled its growth characteristics on four different chitinous substrates as well as two agro-industrial waste products. We found that the enzymatic mixture secreted by FW57 was not only able to digest pre-treated sugarcane bagasse, but also untreated sugarcane bagasse and maize leaves. The efficiency was comparable to a commercial enzymatic cocktail, highlighting the potential of the S. longicolleum enzyme mixture as an alternative pretreatment method. To further characterize the enzymes, which efficiently digested polymers such as cellulose, hemicellulose, pectin, starch, and lignin, we performed in-depth mass spectrometry-based secretome analysis using tryptic peptides from in-gel and in-solution digestions. Depending on the growth conditions, we were able to detect from 442 to 1092 proteins, which were annotated to identify from 134 to 224 putative carbohydrate-active enzymes (CAZymes) in five different families: glycoside hydrolases, auxiliary activities, carbohydrate esterases, polysaccharide lyases, glycosyl transferases, and proteins containing a carbohydrate-binding module, as well as combinations thereof. The FW57 enzyme mixture could be used to replace commercial enzyme cocktails for the digestion of agro-residual substrates.

In recent years, reproduction of human mechanical hand with upgraded abilities is one of the major concerns. This paper addresses the problems of underactuated robotic hand with low cost design as it avoids electromyogram (EMG) sensors. The main goal is to balance the hand in the way, like grabbing, speed, and power, and provide a more robust and cost effective solution. All fingers have some mechanical consistency for picking up objects in a better way. A Flex sensor is attached to all fingers and it is interfaced with a computer using Arduino UNO microcontroller. The sensor aids the arm in three different directions: at first it senses whether an object is grasped or not. In the second step, it determines the coefficient of friction between the objects. Finally it grasps the object and stops. One of the primary issues of prosthetic hand is to have the capacity to satisfy every detail of torque, speed, and latency. In this research, we have developed a model of robotic hand with some modifications. The adaptability of grasping is compared with the degree of freedom (DOF) along with the quantity of fingers. We are controlling our hands via sensors based signal controlling system. The idea is to design a robotic hand, which has low cost, is easy to use, and is light in weight, which helps the amputees to use it with ease in their daily lives. The efficacy of the proposed control is verified and validated using simulations.

Goal orientations of teachers for teaching are significant in shaping educational settings such that they are linked to some school concepts. The curriculum and teaching autonomy of teachers is related to the teachers' goal orientation. Further, school climate that is shaped by behaviors of teachers, students, and administrators affects the goal orientations for teaching. The aim of this study is to reveal the role of teachers' autonomy behavior and perceptions of school climate on their goal orientations for teaching. The study was designed as correlational study. The participants consisted of 284 teachers in public and private schools. Instruments were "goal orientation scale for teaching", "school climate scale", and "teacher autonomy scale". Multiple Hierarchical Regression Analysis method was used to analyze data. The results indicated that school climate was more powerful of and significant predictor of goal orientation than teacher autonomy. Further, teacher autonomy and school climate predicted dimensions of goal orientation for teaching, which were mastery, performance-approach, work-avoidance, and student relations. The study made a contribution to teacher education and school effectiveness literature by revealing factors related to teachers' goal orientations for teaching.

The aim of this study is to investigate the effect of Universal Design for Learning on students' academic achievement and attitudes towards mathematics course, and to reveal the opinions of the students about Universal Design for Learning. In this study, the concurrent embedded strategy was used. The participants consisted of 33 primary school students from two different 4th grade classes at a primary school. In the quantitative part of the study, non-equivalent control groups design was utilized. Data were collected through an academic achievement test and an attitude scale. The findings indicated that Universal Design for Learning had a large effect on academic achievement and attitudes towards mathematics course. In the qualitative part of the study, case study was used and data were collected through a focus group interview. The students participating in the interview were identified with a maximum variation sampling method. The students stated that Universal Design for Learning improved their attention, interest, cooperation, and self-regulation skills. They also stated that Universal Design for Learning supported their retention of knowledge, multiple representations of knowledge, and active participation to lesson. As a result, Universal Design for Learning is thought to be one of the effective methods in mathematic courses and its use is recommended. This study was derived from the master's thesis named "The Effect of Universal Design for Learning on Students Academic Achievement and Attitude towards Mathematic Course".

The purpose of the study is to reveal the effect of cooperative learning on the attitudes and reflective thinking skills of the students' in English course. The study was carried out for 5 weeks with 66 students studying at the 10th grade at an Anatolian high school in the district of Karadeniz Eregli in Zonguldak during the fall term of the 2015-2016 academic year. The design of the study was 'nonequivalent control groups pre-test post-test' which is one of quasi-experimental designs. The scale of attitude towards English course and the scale of reflective thinking were used as instruments of the study which were applied as a pre-test and as a post-test. According to the results, it was concluded that cooperative learning is significantly and positively more effective on students' attitudes and reflective thinking skills than traditional method. This study was produced from master thesis of written by first author under the supervision of second author.

The purpose of this study was to investigate a predictive power of prospective teachers' self-efficacy sources on their teaching self-efficacy and attitude towards the teaching profession. Design of the study was the correlational research. The study was conducted on 315 prospective teachers studying pedagogical formation education in a 2017-18 academic year. Instruments were "Prospective Teachers' Self-Efficacy Sources Scale", "Teaching Self-Efficacy Scale" and "Attitude Scale of Teaching Profession". Pearson Product Moment Correlation Coefficient and PATH analysis methods were used to analyze the data. Results indicated that emotional states, mastery experience, and verbal persuasion were significant predictors of prospective teachers' teaching self-efficacy and attitudes towards teaching profession.

The aim of this study is to develop the English language skills self-efficacy belief scale for higher education students. For this purpose, the trial form has got 48 items. In the fall semester of the 2017-2018 academic year, the trial form was applied to a total of 305 university students. Explanatory factor analysis was conducted to prove the construct validity of the developed scale. For the factor analysis of the data, it is shown that the data set is suitable for factor analysis as the KMO value is 0,96 and the Barlett test significance value is 0,000. The communality values to select items, factor loadings and item total correlations were examined in item selection. The scale is formed as four factors; reading, writing, speaking and listening, and a total of 29 items. In order to determine the appropriateness of the scale to the students of the same level, the scale was applied to 301 preparatory students in School of Foreign Languages of a university. As a result of confirmatory factor analysis, the scale of 22 items, the high item total correlations of the items forming the scale factors show the high structural validity of these scale items. In the confirmation study, the Cronbach Alpha internal consistency coefficients of the scales were 0,82 to 0,91. These values show that the scale provides reliable results, in other words, the scale correctly measures the students' self-efficacy beliefs in 4 basic language skills.