Due the implementation of nanotechnologies in the pharmaceutical industry over the last few decades, new type of cutting-edge formulations—nanopharmaceutics—have been proposed. These comprise pharmaceutical products at the nanoscale, developed from different types of materials with the purpose to, e.g., overcome solubility problems of poorly water-soluble drugs, the pharmacokinetic and pharmacodynamic profiles of known drugs but also of new biomolecules, to modify the release profile of loaded compounds, or to decrease the risk of toxicity by providing site-specific delivery reducing the systemic distribution and thus adverse side effects. To succeed with the development of a nanopharmaceutical formulation, it is first necessary to analyze the type of drug which is to be encapsulated, select the type matrix to load it , followed by the production procedure. Together these elements have to be compatible with the administration route. To be launched onto the market, the selected production method has to be scaled-up, and quality assurance implemented for the product to reach clinical trials, during which in vivo performance is evaluated. Regulatory issues concerning nanopharmaceutics still require expertise for harmonizing legislation and a clear understanding of clinically compliant production methods. The first part of this study addressing “Nanopharmaceutics: Part I—Clinical trials legislation and Good Manufacturing Practices (GMP) of nanotherapeutics in the EU” has been published in Pharmaceutics. This second part complements the study with the discussion about the production scales and clinically compliant production methods of nanopharmaceutics.  A number of emerging nanotechnologies are being exploited in medicine to improve the therapeutic outcome of several drugs and biomolecules and to address unmet medical needs. European Commission aims to lead innovation towards the development of these nanopharmaceutics by launching several funding opportunities within Member States, Associated Countries and Third Countries. The strategic plan for the next Horizon Europe framework programme has clearly set nanomedicines and advanced therapies as priorities. To succeed, the nanoproduct needs to be manufacturable at large scale and its quality assured in order to reach clinical trials. The topic is indeed of high scientific interest considering the number of scientific papers dealing with clinical trials and nanoparticles over the last twenty years .The scientific category “Pharmacology Pharmacy” clearly dominates with 31.432% followed by the “Nanoscience Nanotechnology” with 17.925%. Regulating nanopharmaceutics is a challenge since the selection of the regulatory pathway is governed by the classification of the product which is defined by its type of action [To measure the maturity of the development of a product, Technology Readiness Levels (TRL) can be used . This syThe number of nanopharmaceutics currently on the market is still limited. This is mainly due to difficulties encountered during the processes of scaling-up which reflects on the quantity and even quality of products reaching clinical trials. The transition between the laboratory experimental production to the industry large-scale production is still a challenge in nanopharmaceutics. The laboratory-scale batches produced at the early stage of the developmental process are of very small size , and commonly result from pre-formulation studies, help to define the qualitative and quantitative formulation, and set the production parameters for medium/large scale. Such small batches supply the pre-clinical and/or clinical studies. The pilot scale batches are larger than the laboratory batches and usually support stability studies, and help to optimize production parameters and appropriate equipment. Pilot batches may also supply clinical trials. Industrial scale batches are those produced over the course of the marketing process. The scale-up of nanopharmaceutics may be a little more tricky as the process may affect the properties of the particles which make them singular in comparison to their bulk counterparts, e.g., colloidal stability, the drug loading, the mean particle size, the morphology, and surface properties . The conThe production methods of nanopharmaceutics should ensure that the product has at least one dimension in the nanoscale—from 1 nanometer to 100 nanometers—to conform with the definition ,12. RegaLipid nanoparticles can be of different types . Besides, some metal nanoparticles exhibit antimicrobial activity being commonly applied in coatings for wound treatment ,52. This section details the most commonly used methods for the production of each type of nanoparticles illustrated in High-pressure homogenization (HPH) is a technique with recognized advantage for large-scale production of lipid nanoparticles. Hot homogenization or cold homogenization can be used . In the The membrane contractor method also requires a first step of melting of the solid lipid in which the drug is dispersed or dissolved . This orThe microemulsion method requires the preparation of a microemulsion by dispersing, under mechanical stirring, the melted lipid containing the drug in an aqueous surfactant solution heated up at the same temperature as the organic phase, followed by the dilution in a large volume of cold water (0–4 °C) under magnetic stirring. Lipid nanoparticles are result from the recrystallization of the lipid phase induced by the thermal shock. Although not particularly suited for large-scale production, the microemulsion method is simple, reproducible and suited for sensitive compounds . To prodMultiple emulsion or double method has been proposed for the loading of hydrophilic molecules into lipid matrices . It requThe solvent emulsification diffusion method has been firstly proposed for the production of polymeric nanoparticles , and theThis method is a variation of the solvent emulsification diffusion by replacing the polar protic or aprotic organic solvent by non-polar solvent . It is suited for the loading of lipophilic drugs into the lipid nanoparticles but with higher risk of toxicity due to the type of solvents involved.These methods commonly precede the high-pressure homogenization technique as they are used for the production of the pre-emulsion prior to high-pressure homogenization. If used isolated, they usually need extended times for the dispersion of the melted lipid phase into the hot aqueous surfactant solution for the production of the pre-emulsion, which upon cooling down generates lipid nanoparticles with a higher polydispersity, in comparison to the combination of ultrasound/high-shear homogenization with the high-pressure homogenization ,66,67.The production of polymeric nanoparticles by extrusion method requires the use of polycarbonate membranes and is based on the induced precipitation of drug-loaded nanoparticles at the exit of the nanopores . The metIonic gelation is commonly used in the production of nanoparticles from ionic polymers . Briefly, the method involves two mixed aqueous phases with a following transition from a liquid to a gel—a consequence of ionic interactions occurring at room temperature . The genThe nanoprecipitation technique is a bottom-up method which generates nanoparticles instantaneously using two miscible solvents, one of which does not dissolve the polymer. Firstly, the drug and the polymer are dissolved in one of the solvents . Nanoprecipitation of nanoparticles loaded with drug happen under gentle magnetic stirring by dropwise addition of the solvent in which the polymer is not soluble. This latter solvent is called non-solvent .The salting-out method is a variation of the nanoprecipitation method by replacing the non-solvent by an agent that induces the precipitation of the polymer entrapping the drug. Briefly, the organic solvent in which the polymer and drug are dissolved  is added to an aqueous surfactant solution containing a high concentration of electrolyte as the salting-out agent  to produce an aqueous gel forming oil-in-water emulsion under high mechanical stirring . The dil2) [The supercritical fluid method is based on the extraction of the organic solvent from the inner phase of an oil-in-water (o/w) emulsion using the supercritical carbon dioxide (CO2) . This me2) .Metal-based nanoparticles are produced from bottom-up techniques ,73, eithguideline on the requirements to the chemical and pharmaceutical quality documentation concerning investigational medicinal products in clinical trials”, of the Eudralex, Volume X, is of instrumental value as it displays specifications about the development of IMP  [The scaling-up of a production process requires an absolute control or each and every technical parameter, in such a fashion that only slight differences can be found between different batches of the same nanoproduct. To assist on the development of clinically accepted nanopharmaceutical batches, the “roducts) . ExampleThe nanoproduct to reach clinical trials needs to be carefully identified regarding the Annex 13 of IMP. Besides, the number of individuals enrolled in the clinical trial must be critically defined so that the tested batch size is aligned with the respective phase of the trial. In Phase I of clinical trials, the group size is usually between 10 and 100 . Phases Once the clinical trials are finished, the results are included in the IMPD to further submission for marketing introduction authorization . In EuroOver the last decades, technological developments gave birth to a new class of products, the so-called nanopharmaceutics. These formulations represent a step forward to innovative personalized medicines with improved outcomes to patients and to public health systems. However, with innovation and modernization, come also other issues related to regulatory affairs requiring new legislation to shape them for human use, which should cover quality, efficacy and safety of the product before it reaches clinical trials. A positive benefit/risk relationship must be ensured.The internationally available guidelines for clinical trials and the required IMPD are instrumental to ensure that the product submitted to an evaluation for a marketing introduction authorization is reliable. While the production of nanopharmaceutics are strongly tied to GMP, continuous scientific guidance is still required to ensure quality and safety. As the methods for production of nanopharmaceutics differ amongst the advantages and easiness to be scaled-up, the selection should also rely on the safety of the final product. Bioactives from natural sources with nutraceutical value  are of focused interest and are being proposed as ingredients to be loaded into nanoparticles to obtain a new production—nanonutraceutics. The added value of several nutraceuticals in the prevention, treatment or delay the onset of a disease is very well documented ,82,83,84Nutraceutics, which derive from food matrices of vegetal or animal matrices, are a novel toolbox not yet completely explored for its full potential in medicine. Current research is looking towards several nanotechnological approaches to be exploited for the formulation of nutraceuticals ,91,92,93Nanopharmaceutics emerged as a promising technology in pharmaceutical industry due to their unique properties resulting from the size, shape, morphology and surface properties, which are effective only if essential parameters such as quality, safety and efficacy are ensured over the course of the scale-up process. However, no standard methodology is available to control the quality of these nanoproducts in a scale-up process. Nanopharmaceutics triggered an entire revolution in pharmaceutical industry with significant impact also on nutraceutics, which are attracting growing interest for their beneficial health effects, resulting from improved delivery, enhanced bioavailability and biological effect. Although several tremendous investments from industrial stakeholders have already been made, future outcomes will positively accompany the modifications in the way these products are controlled, produced, and launched on the market, through safe and effective filtering out of the non-compliant products and preventing them going to the market, and facilitating the good products being made available to the public, as well as stimulating the developments of even newer, and improved products.


The adsorption of superplasticizer molecules to particle surfaces in cementitious systems is a very important aspect for the desired liquefaction of pastes and concretes. This way, the comb shaped polymers shield attractive forces between the particles and induce a well-dispersed, homogeneous suspension. These admixtures allow the usage of fine fillers even in combination with low amounts of mixing water, and thus, are the basis for modern high performance concretes. However, the adsorption does not cause beneficial effects only: The polymer covered particle surfaces, especially clinker, are hindered to interact with water, thus hydration is retarded. This is the reason for lower early strength and is very disadvantageous for certain applications. Today it is known that the molecular structure of the polymers, for instance the chain length and charge density, affects the retardation strongly. The complexity and diversity of cementitious systems is the main reason why research in this field is quite empiric and time as well as cost intensive. To investigate the adsorption of superplasticizers in various systems in-situ, a fluorescence microscopic approach was applied: By staining the polymers with fluorescent dye they become localizable and the adsorption quantifiable. This work shows the influence of molecular structure to adsorption characteristic of different polymers and the correlation to the retarding effect of superplasticizers, especially concerning the presence of silica fume, which is indispensable for ultra-high performance concrete (UHPC). The o the PCE . Similarle salts ,11. Depele salts . Becausele salts , generalle salts . Furtherle salts ,17. Besile salts ,21,22,23le salts . In the le salts  were corsilicol P  was used.This work is focused on the hydration of an ordinary Portland cement (OPC), Type CEM I 52,5 HS/NA  acc. DIN EN 197-1, which is primary used in UHPCs developed in the German priority program 1182 funded by the Deutsche Forschungsgemeinschaft (DFG) , especiaThe results of the x-ray fluorescence analysis (XFA) for the used cement and the silica fume are shown in The superplasticizers were synthesized and characterized by the research group of Prof. Plank at the Department of Chemistry at Technical University Munich, Germany. The first one is an allyl ether based comb polymer containing maleic anhydride monomers APEG, a. In theThe most common way to investigate the hydration of cement is to measure the heat flow that is produced by the physicochemical reaction. Therefore, samples with varying silica fume to cement ratio, superplasticizer-type and superplasticizer content were investigated. If superplasticizers are used, the w/b ratio was kept at 0.25 and the water content of the superplasticizer solution was considered in the total amount of mixing water. As reference, samples without superplasticizers were mixed. To enable a homogenous mixture, the w/b ratio was 0.4 in this case. The detailed sample composition is shown in For the staining process a chemical procedure, which requires an organic solvent (toluene) for the staining reaction as well as a two-step reaction for binding the rhodamine B-dye to the polymers, was used . By usinWith the used fluorescence stereoscope M 205 FA  and the sample holder described in  diluted The heat flow during the first four days after mixing of the pure cement samples with APEG and MPEG in different dosages  are showThe investigated cement shows two almost overlapping hydration peaks, while for samples with PCE only one is indicated. In general, the first peak of pure cement’s heat flow is caused by the precipitation of C–S–H; the second is due to the formation aluminate phases . DependiThe calorimetrical results point out a clear dependency of the hydration retardation to the used PCE. The higher the PCE is dosed, the more the hydration is retarded. For common dosages of superplasticizers (>0.5 %bwob) this finding stands in line for example with work by Zingg  and is cIn In the presence of silica fume, the two hydration peaks turn to a main peak with a little shoulder. Again, samples with PCE show only one peak. The presence of silica fume reduces the retardation remarkably in four of five cases: Without any polymer, the hydration peak of the sample reaches the maximum about two hours earlier than pure cement. For 1.2 %bwob MPEG the time reduction is quite significant, but especially for 0.5 %bwob APEG it is remarkably reduced by more than 50 h. In contrast, the hydration is still completely inhibited if the APEG dosage is 1.2 %bwob. With the addition of silica fume, the total specific surface of the system increases. This issue will be discussed in detail in For better illustration of the peak shifts, the signal intensities of the excitation and the emission spectra in The microscopic images of the samples with stained APEG and MPEG show different kinds of mineral particles: Electron microscopic investigation of the coarse cement showed sharp, broken particles (clinker) and rounder ones, which are characterized by energy dispersive x-ray spectroscopy (EDX) to be calcium sulfate. Via XRD analysis, both gypsum and hemihydrate were detected, but within the fluorescence microscopic experiment, both could not be differentiated. Thus, these particles are called gypsum in the following, although it cannot be excluded that hemihydrate particles were investigated as well. The strong fluorescence signal of the round particles indicates a high amount of adsorbed PCE, while the adsorption on the clinker particles is much more heterogeneous. The time resolved image series  indicateThe gypsum particles are identifiable by color and shape in light microscopy as well, thus, measurements without PCE (just coarse cement in pore solution) were made to investigate the dissolving speed. For the used experimental parameter, the gypsum dissolves fast and the white particles disappear within 30–40 min .3A with sulfate [3A-surfaces seem to be passivated. At this point it is not clear, if this passivation is based on immediate precipitation of ettringite after mixing cement and PCE-solution, before the measurement could have been started, or by a PCE-coverage. On the one hand, this issue should not have major influence on the experiment because of the low C3A-content of the used cement, but on the other hand, Considering the proven bound of the dye to the PCEs, the most polymer adsorbs in case of pure cement to the gypsum particles shown by the bright signal in the fluorescence images for these experimental conditions. It is known that SP interact strongly with very early hydrate phases such as ettringite, which forms rapidly by the reaction of C sulfate . In the However, although often investigated in terms of early hydration, e.g., by Stroh , the for2+) [To take the chemical structure of APEG and MPEG into account, the main differences are the chain lengths and the charge density of the backbone as a result of the different polymerized monomers. In literature, the charge density of APEG is given as 3700 µeq/g at a pH value of 13 , while M2+) . In this2+)  and MPEG2+) , thus, t2+) . However2+)  and promAlthough the suspensions of cement with silica fume were mixed carefully prior to the measurements, the images are dominated by round agglomerates of silica fume with sizes up to 100 µm, and cement particles are hard to identify. Moreover, the images are somewhat diffused as a consequence of dispersed silica particles, which cannot be resolved by the stereoscope. Nevertheless, a very strong adsorption of both PCEs to the silica fume is indicated by the bright fluorescence of the agglomerates . In caseIn contrast to the measurements of the cement suspensions without silica fume, no significant changes of the fluorescence images over time could be noticed.The reason why no time dependent changes of the fluorescence images were found, for example precipitating hydrate phases, is the overall bad interpretability of the fluorescence microscopic data caused by the silica fume. Up to this point, it is not quite clear how far this issue may be overcome by improved preparation setups and microscopic methods in the future. Even if single silica particles could be resolved for example with confocal laser scanning microscopy (CLSM), the Brownian motion would lead to blurry images.The experimental setups and conditions for the cement hydration of both used methods in this work are quite different. Thus, a quantitative comparison is not appropriate, but it could be shown that qualitative prediction via fluorescence microscopic analyses concerning the retarding effect of superplasticizers are plausible.3A content in combination with varying PCEs (structure and dosage) should be taken into account.For the effective use of UHPC in various applications, well-adjusted systems are of major importance. Concerning special characteristics and requirements of UHPC mixing, it is essential to use optimized and balanced admixtures to face certain challenges. For example, silica fume is needed as pozzolanic and filling material but may cause strong incompatibilities with superplasticizers  and ofteAs a compromise between effort and research progress, the fluorescence microscopic method reveals information about the interactions of additives and mineral systems in real time to find the best adjustments for the versatile construction material.


Phlebotomy is one of the most important steps in the preanalytical phase of a clinical laboratory process. In order to decrease phlebotomy errors, this specific procedure should be taught in detail by laboratory organizations. Our study aims to practice the training program on venous blood sampling and observe the close follow-up results.In this observational study, 127 students who started their summer internship in Antalya Education and Research Hospital were given a one-day theoretical phlebotomy training in accordance with the Venous Blood Sampling Guidelines. After the theoretical training, phlebotomy applications of 10 students who were working in the field of out-patient blood sampling were observed both with and without their knowledge. A comprehensive checklist related to phlebotomy was created by the trainers in Antalya Education and Research Hospital and the observers answered each question as yes or no. For the statistical analysis, IBM SPSS Statistics 21.0 was used.After the theoretical education, the trainees were observed but no significant difference was found between the first and the second informed observations (p = 0.125). The students were observed three times more in the following week without their knowledge. There was a statistically significant difference between the first and the third unannounced observations (p=0.001).In order to perform phlebotomy correctly, apart from theoretical education, a close follow-up is necessary too. Venous blood sampling is one of the most important steps of the preanalytical phase and is the most common invasive procedure in health care. Every step it consists of has a potential risk for patient safety Phlebotomy is a composite process that needs accuracy, ability, responsibility and good interaction between the patient and the phlebotomist. It also demands manual skills and theoretical knowledge given in the guideline Moreover, phlebotomy errors can often go unrecognized, since they are latent and distant from direct control This observational survey study was performed in July 2017. A group of 127 students who started summer internships in Antalya Education and Research Hospital (AERH) took part in an education program about phlebotomy in accordance with the Venous Blood Sampling Guideline. After the education program was completed, 10 of the students practising in the out-patient blood sampling area were included for the close follow-up part of the study. All the students who were educated (n=127) came from different health colleges of Antalya, and although legislation of colleges included guidelines for venous blood sampling, compliance training was provided when they came to our hospital.All students were invited to a one-day theoretical education programme about venous blood sampling. This programme was planned based on the Venous Blood Sampling Guideline published by the Turkish Biochemical Society After the theoretical program, students started their internship in different areas of the hospital. The second step of our study included 10 students practising only in an out-patient blood sampling area. Following the theoretical education, the phlebotomy practice of those 10 students was monitored. For each student, five observations on different days were done while they performed venipuncture.The first informed observation was carried out during blood collection with the knowledge of the student. A comprehensive checklist containing 10 important steps involved in venipuncture technique was created by educators at AERH . The queOn the third occasion, one week later, they performed blood collection without knowing that they were being observed. After the procedure, the student was informed that he/she had been observed during this process and also was informed about the mistakes they had made. Unannounced observations were repeated two more times on different days until a minimum of errors occurred.Results are presented as counts and percentages. Differences between observations were analysed with Cochran's Q test. A p-value 0.05 was considered as statistically significant. Different groups were evaluated with a Bonferroni corrected McNemar test During the week following the theoretical training, the students were observed for their blood sampling performance. When two consecutive correct venipunctures were performed, the observation was terminated for that day. Ten students who were interns in the outpatient clinic were observed for their venipuncture performance the day after the theoretical education. All students performed nearly correct blood sampling according to the guideline . In the In the second phase of our hands-on training, we observed the students without their knowledge in the following week. In the first unannounced observation, only four of the ten students performed correct patient identification, six of ten cleaned the venipuncture site correctly, six of ten students had repalpation, six students removed the tourniquet at the appropriate time, only two of them followed the correct order of draw according to the guidelines, four of ten inverted the tubes for sufficient number of times. After each observation, we described the mistakes they had made in that process. When two consecutive correct venipunctures were performed, the observation was terminated for that day .During the second unannounced observation, four of ten students identified the patient correctly, eight of ten cleaned the venipuncture site correctly, six of ten students had repalpation, four students removed the tourniquet at the appropriate time, eight students followed the correct order of draw according to the guidelines, and two of ten inverted the tubes for sufficient number of times. All students were immediately notified of their errors .In the last unannounced observation, six of ten students performed correct patient identification, four students had repalpation, eight students removed the tourniquet at the appropriate time, and only four of ten did perform inversion of the tubes for sufficient number of times .In all informed and uninformed observations, all the students placed the tourniquet correctly at 4 finger width (10 cm) above the venipuncture site, performed venipuncture properly, performed sampling with correct blood volume, and needles/collection systems were safely and immediately disposed into a sharps container.There were very few errors in the first informed observation. There were no errors in the second one. Consequently, there was no statistically significant difference between the first and second informed observations (p = 0.125) . When alWe believe this is the first observational study which focused on the change in performance of phlebotomy after a close follow-up of the trainees besides theoretical education. The inspiration for the study came from our experience that students consistently make errors when performing venipuncture in spite of all training, both theoretical and practical.Laboratory methods change over time, and this can lead to a need to adapt venipuncture procedures and consequently, further training can help to improve practices This study also provides practical strategies for measuring changes in skills and knowledge. Some of these can be used to evaluate the effectiveness of undergraduate and graduate education programs Patient safety related to patient identification is a controllable challenge in all types of blood collection procedures In our hospital, a phlebotomist uses the IDlabelled tubes the laboratory secretary has already labelled. The students observed in this study should have checked if the tubes belonged to the patient he/she was going to perform the phlebotomy on. Therefore, in this study, patient identification was not a big challenge for a phlebotomist, but they had to confirm that the tubes actually belong to the patient whose blood was taken. In the informed observations, the students were careful about our patient identification procedure, but in the first unannounced observation only 40% of them confirmed that the labelled tubes belonged to the patient. After a close follow-up, on the third unannounced observations again only 60% did the confirmation of the patient identification . SimilarAnother important issue in this study was that students would not invert the tubes for sufficient times. Regarding mixing/inverting the test tubes after blood sampling, the majority followed the guidelines; but on the first and the third unannounced observations, there were 40% of students who did proper mixing. On the other hand, the interns were inverting all the tubes but the inverting times for most tubes were below the standard. Likewise, in a previous study in China, the inverting times for most of the tubes were below the standard; therefore, the researches commented that only 22.5% of the nurses were aware of the correct definition of an inversion The other three steps of phlebotomy that the students had problems with but had improved after a close follow-up training were about repalpation, removing the tourniquet at the appropriate time and following the correct order of draw. In the third unannounced observation, all the students cleaned the venipuncture site properly, but only six of them (60 %) left the venipuncture site untouched after cleaning. Even though they knew that it should be intact, we observed that the students were not self-confident enough and they could not stop themselves from repalpation.In our study, the correct rate on tourniquet release time was 80 % at the end of the close followup training, 60 % on the first unannounced observation, and 100 % on the informed observation. However, previous studies demonstrated that less than 50% of their phlebotomists did not release the tourniquet when the first test-tube had blood inflows The other four steps of phlebotomy observed in our study were well done by the students and all ten of them placed the tourniquet 4 fingers (10 cm) widths above the venipuncture site, performed the venipuncture according to the guidelines, collected the proper amount of blood, and disposed of collection systems immediately .The difference in the adherence to some of the guidelines practice in phlebotomy may be due to different education of the phlebotomy staff, various settings and relations There are some limitations to our study. First of all, the questionnaire for investigation was selfdesigned without undergoing a thorough reliability and validity test. Another limitation was that the long term effects of our close follow-up could not be observed because the students' internship ended in a short time.As a conclusion of the study, the rate of error was reasonably low in the informed observations of students who undertook a theoretical education on phlebotomy, but this rate increased significantly on the following days where the same students were observed with unannounced observations . After aAcknowledgements. This article is a detailed summary of an oral presentation delivered during the International Biochemistry Congress 2017 /28 the National Biochemistry Congress. Ataturk University Erzurum (Turkey),19–23 September 2017.The authors state that they have no conflicts of interest regarding the publication of this article.


Tumors are driven by a sequence of genetic and epigenetic alterations. Previous studies have mostly focused on the roles of somatic mutations in tumorigenesis, but how germline variants act is largely unknown. In this study, we hypothesized that allelic expression imbalance (AEI) participated in the process of germline variants on tumorigenesis. We screened single-nucleotide polymorphisms (SNPs) as representative germline variants. By using 127 patients’ RNA sequencing data from paired lung cancer and adjacent normal tissues from public databases, we analyzed the effects of the functional consequence of SNPs, function and conservativeness on genes with AEI. We found that natural selection can affect AEI. Functional adaptability of genes with a high frequency of AEI and a correlation of the incidence of AEI with conservativeness were observed in both adjacent tissues and tumor tissues. Moreover, we observed a higher incidence of AEI in genes with non-synonymous SNPs than in those with synonymous SNPs. However, we also found that AEI was affected by allele expression noise, especially in tumor tissues, which led to an increased proportion of AEI, weakened the effect of natural selection and eliminated the influence of the functional consequence of SNPs on AEI. We unveiled a previously unknown adaptive regulatory mechanism in which the effect of natural selection on SNPs can be reflected in allelic expression, which provides insight into a better understanding of cancer evolution. Tumorigenesis is a process in which normal cells transform into cancer cells, manifesting as a cellular accumulation of changes at the genetic and epigenetic levels and eventually leading to uncontrolled proliferation. This process is similar to evolution, which acts through mutation accumulation and then selection . A smallAPC , and also analyzed RNA sequencing data from a South Korean dataset  for verip-value = 1.68E–37, p-value = 5.55E–14, We found that the incidence of AEI was significantly higher in cancer samples than in adjacent samples for both TCGA    . Notably1.06E−8) .Evolution means a change in the allele frequency, even a loss of the allele . AEI is We hypothesized that AEI could be subject to natural selection. We defined the frequency of AEI as the percentage of individuals who had AEI in the specific genes, and we considered only genes that were expressed in at least 20% of patients. The frequencies of AEI are displayed in We used DAVID   to studyIn addition, genes in the tumor group were enriched in stress reactions and DNA repair but in the adjacent group were not  was used to represent conservation at the gene level . Genes wTaken together, we found a significant correlation between conservation and the frequency of AEI at both the nucleotide level and the gene level in adjacent samples but not in tumor samples.From the above analyses, we know that AEI occurs more frequently in tumor samples, which seems to indicate that tumors are affected more strongly by natural selection than normal tissue, but the decreased correlation between AEI and gene function, as well as conservation, indicates that tumors are less affected. If only natural selection is used to explain these results, there will be conflicts. We hypothesized that other factors must also affect AEI.Even if two cells have the same genotype and external environment, the two cells can still have different phenotypes, and this phenomenon is called gene expression noise . Comparet-test also suggested the there was no significant difference between the ratio in tumor samples and in adjacent samples . The results demonstrated that the allele ratio did not significantly change in the tumor, and tumor tissues were not subjected to stronger selection than adjacent tissues. Moreover, the CV of the ratio of major to minor shifted on the x-axis . We speculated that the elevated incidence of AEI in tumors resulted from a stochastic process.We screened the AEI genes in adjacent tissues and calculated the expression ratio of the two alleles. We defined the allele that had a higher allele expression ratio in the majority of adjacent tissues as the major allele and the other as the minor allele. Then, we calculated the mean and the coefficient of variation (CV) of the ratio of the major allele to the minor allele (major/minor) for every AEI gene expressed in at least 10 paired samples from TCGA data. Because CV represents the degree of heterogeneity of the allele expression ratio of the genes in different samples, we used CV as a measure of noise. The scatterplot of the mean major/minor  showed te x-axis , indicatTo further examine the choice of allele expression in tumors, we grouped genes according to whether the SNPs they carried changed the amino acid sequence, and we analyzed the incidence of AEI in the tumor tissues and adjacent tissues of patients.p-value = 0.007869 in South Korean patients and p-value = 4.26E−9 in TCGA data); however, this difference could not be observed in tumor samples   . The proIn the previous analysis, we found that allele bias has a limited effect on the overall incidence of AEI, but during the evolution of the tumor, natural selection enriches the cell population with growth dominance. Therefore, it is also possible to enrich certain alleles at the chromosome level. Thus, it is still unknown whether the effect of natural selection on the cell population will affect the expression of transcriptome alleles by selecting the proportion of non-synonymous SNP alleles at the chromosome level.p-value = 0.699 for tumor samples and p-value = 0.150 for adjacent samples) , but this was not observed in tumor samples . We further calculated the proportion of AEI in genes with synonymous and non-synonymous SNPs in data from the “1000 Genomes” project as normal control data since the adjacent tissue may carry tumor-related characteristics. In the “1000 Genomes” project data, we also observed a significant increase in the proportion of AEI in genes with non-synonymous SNPs  . MoreoveConsistent with a higher replacement rate of non-synonymous mutations, the higher incidence of AEI in genes with non-synonymous SNPs in normal tissues indicates that these variations undergo selection pressure. However, in the case of a higher incidence of AEI in tumors, the promotion of AEI in genes with non-synonymous SNPs could not be observed, indicating that instead of being subjected to selection pressure, AEI in tumors reflects increased noise.The current evolutionary research is based on the theory that natural selection directionally influences population allele frequency. AEI is an ideal tool for the observation of selection characteristics at the transcriptome level. Therefore, in this study, we used AEI to analyze the selection characteristics of lung cancer tissues and normal tissues from different aspects of the transcriptome.The results from the gene function cluster analysis displayed the differences in the functional adaptation of AEI in tumor tissues and adjacent tissues, with immune-related genes being enriched in the adjacent tissues and DNA-repair-related and stress response genes being enriched in the tumor tissues. Inflammation and lymphocyte infiltration are widely present in the tumor and adjacent tissues . We founConserved genes are generally required for important biological functions, and cancer driver genes often show a higher degree of conservation . The negcis-regulatory effects through natural selection.SNPs are generally considered to be neutral and relatively stable polymorphisms in the current population. Their influence on regulation is generally also thought to be through transcription factor binding in the transcriptional regulatory region outside the coding region, while stable variation in the exon region directly affects gene regulation. However, in our research, we found that whether the exon SNP changes the amino acid affects the expression ratio of alleles. In the transcriptome data of adjacent tissues and in the 1000 Genomes project data, genes carrying non-synonymous SNPs are significantly more likely to have a higher proportion of AEI than those carrying only synonymous SNPs. In evolutionary analysis, the synonymous substitution rate and the non-synonymous substitution rate of genes are often used as measures of the selection of genes . ApplyinHowever, in our analyses, we found that, compared with normal tissues, the correlation between AEI and gene function as well as conservation in tumor tissues showed a weakening trend. In our results, functional fitness can cluster into only very few items. In the correlation analysis between site conservation and the frequency of AEI, association and confidence also showed a decreasing trend in tumor tissues. No significant association of gene conservation and the frequency of AEI was observed in tumors. These results indicate that the selective effect of tumor tissue on the level of AEI is weakened.Gene expression regulation and stochastic gene expression are the main factors previously thought to have an impact on AEI . The ave1 and were identified by screening according to the following criteria: cases in TCGA-LUAD project that have paired primary solid tumor and Solid Tissue Normal for which each sample has both an RNA-seq BAM file and a WXS (or WGS) BAM file. The details for the cases and barcodes are listed in TCGA sample data: The data used in this study were obtained from the Genomic Data Commons Data Portal2 under accession number GSE40419. We screened 71 patients with cancer with adjacent tissue paired sequencing data and exome sequencing data. Among these patients, 32 (45.07%) were females, and 31 (43.66%) were non-smokers. The publication indexes of the patients are listed in The samples from different primary lung adenocarcinoma stages from South Korean patients published by 3. We downloaded all the sample data from three populations from Finnish in Finland (FIN), British in England and Scotland (GBR) and Toscani in Italia (TSI) in the Geuvadis RNA sequencing project.1000 Genomes data: 4 , where n is the total number of reads that covered the SNP and q equals 0.5 if there is no AEI. We screened the heterozygous SNPs from the annotated results and denoted Rx as the reference allelic read count and Ax as the alternate allelic read count for the SNPs. We excluded loci with Rx < 4 or Ax < 4 or (Rx + Ax) < 20. If gene X was not displayed under balanced allelic expression, Rx and Ax followed the binomial distribution B . A binomial test was used to screen the AEI genes from the read count. If the As the number of effective loci whose DNA and RNA sequencing data both meet the analysis requirements is too small to support subsequent AEI frequency analysis, and from the previous results of the functional consequence of SNPs, the removal of genes that displayed allele ratio bias does not affect; therefore, we used RNA data directly in most analyses.7  to Macaque Orthologs in BioMart from Ensembl. AEI genes that had dN/dS data were screened, and the frequency of AEI was examined based on the associations with dN/dS.We obtained the dN and dS data via orthologous sequencing of All datasets generated for this study are included in the article/JH, XK, and YZ designed the study. JH performed the main bioinformatics analyses. YCZ, MW, YZ, ZX, and MJ provided support. JH, QM, and YHZ wrote the manuscript. All the authors gave final approval for the publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions. The deployment of autonomous vehicles (AVs) can provide benefits such as cost reductions along with improving accessibility to transportation services via decreased travel costs. However, there are some end users’ concerns, regarding the Safety and Robustness of the AVs. The prospective passengers fear several possible instances that could arise in case there is no driver in the bus. Among them, there is the feeling of discomfort being all alone in the bus at night, especially in certain neighborhoods, the fact that no authority figure is present to keep passengers calm or to perform first aid if required and handle abnormal events such as vandalism, etc. To address the aforementioned concerns on social and personal safety and security in the vehicle, certain measures need to be implemented. A solution for enhancing the safety and security inside the autonomous buses will support protecting passengers and safekeeping the vehicles.Automatic awareness of human actions and their interaction with the environment has become a prominent study area in recent years. To perform such a demanding mission, many scientific areas rely on modeling human activity in its various dimensions . In this context, the identification of a person’s activity tends to be necessary to the comprehension of specific acts. Thus, human action recognition has gained a lot of interest, especially in real-world environments. So far, several methods are based on multiple dimension pose datasets and color frame sequences in order to recognize actions. Methods based on colored videos ,2,3,4 tysupports end-to-end detection of abnormal events,achieves real-time inference on modern hardware,offers flexibility with supervised, unsupervised and semi-supervised learning, to compensate the scarcity of data in the security domain,supports multiple camera types, positions and angles,is able to operate in embedded setup with limited power requirements.In this paper, various techniques were implemented and compared for the timely, accurate, robust, and automatic detection of various petty crime types as well as for the assistance of authorized end-users towards the re-identification of any offenders. The proposed solution can identify abnormal passenger behavior such as vandalism and accidents  but can also enhance passenger security via petty crimes detection . In this research, the goal is to define an appropriate solution, able to address such complications using modern technologies and deep learning techniques. The solution:In Mahadevan et al.  proposedMost of the aforementioned techniques are heavily tuned in the surveillance of large areas, while others are using multiple input from other sensors , which are not available in our application as we rely only on 2D data from the camera sensors. Trajectory-based methods are providing limited results, when applied to camera with restricted field of view, since heavy preprocessing  is needed to eliminate false information which may confuse the model. In addition, as mentioned in There is a restrained tendency on sharing data related to security applications. Surveillance data are considered sensitive and related to confidential and legal issues. Moreover, the few available data are often tailored to a specific application and require adaptations in order to be useful. The methods and experiments we developed require a large amount of data due to the nature of the machine learning algorithms. To overcome this obstacle, we performed several data capture sessions on both simulated and real-world scenarios. Therefore, our solution is based mostly in our own data in order to be accurate and satisfy the design constraints. In addition, based on some techniques for data augmentation and methods associated to weak supervision, this issue was sufficiently mitigated.The petty crimes that are targeted for identification by the sensors include petty theft like bag-snatching and pickpocketing, vandalism and aggression. While a commuter is in the autonomous shuttle a petty crime takes place in the form of assault. The commuter is attacked by another person  who is attempting to snatch his bag (Use Case 2: Bag-Snatching). The aggressive incident is captured by the sensors in the shuttle and fed to the video analytics component for further analysis. At a later stage, the system is capable of sending a security alert to the operator or security supervisor. The course of action of the operator is a human decision, that means, whether he/she will decide to stop the autonomous shuttle or will notify the passengers via the radio system. A third use case focuses on the detection of vandalism . A young person boards the autonomous shuttle during an itinerary performed by the vehicle during the night hours. The person attempts to perform a vandalism action on the shuttle, through painting graffiti or trying to smash the windows. The night mode of the cameras installed in the vehicle acquire the data that will be fed to the video analytics algorithms for further analysis. The person is warned by the radio system of the autonomous shuttle or security personnel intervenes by stopping the bus.In this paper, five datasets were used: (a) data simulated in lab, (b) data captured from Geneva Public Transport (TPG) shuttles, (c) from petty criminality diminution through search and analysis in multi-source video capturing and archiving platform (P-REACT)  project,This tool was designed to improve productivity as it automates many procedures related to the time-consuming labeling process. The utility integrates the service’s critical components, such as the pose estimation backend and uses the same data format and structure. The simple and intuitive graphical user interface supports batch navigation, per frame or skeleton labeling and class switching.The following design principles were found to be important during the design of our system: (a) The solution should feature real-time detection when deployed on modern hardware available to the market and (b) the solution should support multiple camera types including wide-angle lens with higher fields of view (FoV). In this paper, three different approaches were implemented: (a) a Stacked Bidirectional LSTM Classifier, (b) a Spatiotemporal Autoencoder, and (c) a Spatiotemporal LSTM Classifier.The first approach consists of a stacked LSTM model as a classifier. An overview of the pipeline is depicted in For the pose estimation stage we adopted the Regional Multi-person Pose Estimation (RMPE) by Fang et al.  with a pIn the tracking stage, we perform matching of cross-frame poses and form pose flows, using a real-time algorithm that is based on a distance matrix. In addition, a pose flow non-maximum suppression is applied, in order to reduce unnecessary pose flows and re-link temporal disjoint ones. This is an important step that associates poses indicating the same person, across multiple frames. We implemented a skeleton tracking algorithm, in order to meet the performance requirements of a real-time service. The algorithm is sorting the skeletons based on the distance between neck and image center, from small to large. Certain heuristics are taken into consideration, such as the position of the joints, the average height of the person and the height difference between frames. Height variation improves the ability of the algorithm to understand depth, since we are based on two dimensional input. Such parameters of the algorithms are optimized for in-shuttle space and fine-tuned to specified weights based on the camera calibration.A skeleton near center will be processed first and be given a smaller human ID. Later on, each skeleton’s features will be matched based on its previous and current frame. The distance matrix (or cost) between the skeleton joints is the main criterion for the matching function. Skeletons with the less distance are matched between the frames and are given the same ID .In some cases, the skeleton detection framework might fail to detect a complete human skeleton from the image due to the camera’s restricted field of view in the autonomous vehicle. Two examples of such situations are highlighted in Events of occlusion can also cause missing joint positions, which should be filled with some values in order to maintain a fixed-size feature vector for the following feature classification procedure. To address this issue we fill in a joint’s position based on its relative position in the previous frame with respect to the neck. We also evaluated some other options with worse results: (a) Discard this frame. However, the algorithm would never be able to detect the action when the person is standing sideways and not facing the camera. (b) Fill in the positions with some value outside a reasonable range. Theoretically, when the classifier is strong enough, this method could work.An example of joint reconstruction is illustrated in N frames  buffer double-ended queue (deque) of The feature vector consists of features described in We consider the buffer as invalid if the newest appended skeleton does not contain at least the neck (Point 0) or one of the thigh bones (Point 7 or 10), as the height of the skeleton  cannot be calculated. The feature extraction process occurs when the buffer is full.N frames for the feature generation along with the evaluation accuracy are depicted in The number of The model is capable of binary or multi-class softmax classification. It contains three hidden layers of size .W, the output size will be In a deep CNN, the main objective of convolution is to extract information from the input frame. The convolution process maintains the spatial relations of pixels by using kernels to extract low level features. In raw mathematics, the convolution operation performs dot products across filters of partial input. Supposing an At this stage, our task is to transform the raw information into the correct input for our model. To do so, we perform alignment of each frame extracted from the raw videos, followed by a resize operation to the resolution of  a spatial autoencoder in order to obtain spatial features and (b) a temporal encoder-decoder to learn temporal features of the encoded spatial features. In particular, the spatial autoencoder consists of an encoder and a decoder that are composed of two convolutional and transpose convolutional layers, respectively, whereas the temporal encoder is comprised of three convolutional LSTM layers, as depicted in t denotes the frame in a sequence:We define the reconstruction error as the Euclidean distance across an input and a reconstructed frame . SpecifiOnce the model is trained, its performance can be evaluated by feeding in testing data and checking whether it can detect abnormal events while maintaining a low false alarm rate. For a better comparison, we calculated the regularity score for all frames employing the same formula as . Our onlUsing a threshold on the reconstruction error, we are able to determine whether a video frame is normal or abnormal . A predeEven if we train our model on thousands of data, we will still get some false positives in certain occasions. As a result that we are able to manually shift through the anomaly outputs and flag some of them as false positives, we can let the previous autoencoder neural network model act as a high recaller. We employ semi-supervised learning by decreasing the threshold, so that we can detect the majority of true anomalies , as well as other false positives (low precision) . To achiIn real-time inference, the anomalies predicted by the autoencoder neural network model  are sent through the false positive reduction model (hybrid model). Combining these two neural networks, should provide a deep neural network model that offers both high precision and high recall. Furthermore, we no longer depend on a prone to error threshold value for classifying events. We refer to this technique as a weak supervision method.The training process of the new experiment consists of 3 stages, as We trained the hybrid model for 20 epochs, using an 64/16/20 train/validation/test ratio. The loss and the accuracy are depicted in The environment for the experiments consist of an Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20 GHz with a TDP rated to 85 W . The operating system is the Red Hat Enterprise Linux Server 7.4 × 86_64. The system utilizes 2 × Tesla K40 m GPUs for acceleration. The Total Graphics Power (TGP) of each GPU is rated to 235 W. The system supports most off-the-shelf cameras. The dimensions and the weight of the sensors vary and the mounting is performed manually. In terms of resolution, the sensor provides 1080p (FullHD) resolution images (1920 × 1080 pixels) at 24 frames per second and the operating temperature range applies . Sensors are powered via the USB 2.0 and Power over Ethernet (PoE) connections. The RGB video stream is set to 24-bit FullHD 1080p resolution. The data rate is configured at 30 Hz but can be adjusted on demand. The data stream is continuous and can be also acquired on request. The API and the SDK of the camera are flexible enough to acquire the images as soon as they are required on the aforementioned maximum data epoch rate. As We tested our pose classification approach on the NTU RGB+D dataset  Figure a–c and oThe following Equations –7) desc desc7) dBased on a recent survey by Zhang et al. , we can Moving on to the autoencoder-based techniques, we trained our models on the P-REACT and TPG datasets. For the sake of completeness, we also train and compare our model with similar methods on the UCSD-Ped1 dataset . The appIn order to visualize the predictions, we provide the preprocessed input frame for the current moment at the bottom-left. The frame is resized from 64 × 64 and grayscale and a mask overlay is obscuring the out-of-interest areas (road). At the right next to the input frame, the resized output of our model is shown. The third mini-frame demonstrates only the significant differences between the input and the output frames with white pixels which is then merged above the original frame for demonstration purposes. Experimental results on test data are enlisted in Since we are targeting an autonomous platform, we need to evaluate the efficiency of this system in terms of performance and power requirements. We obtained the approximate GPU power consumption  using thIn this research, various techniques were implemented for the timely, accurate, robust, and automatic detection of abnormal events. Three different approaches were presented for anomaly detection specifically tailored to the perspective of the final camera setup and the technical specifications of the lens/sensor . Pose cl


A challenging rescue task for the underground disaster is to guide survivors in getting away from the dangerous area quickly. To address the issue, an escape guidance path developing method is proposed based on anisotropic underground wireless sensor networks under the condition of sparse anchor nodes. Firstly, a hybrid channel model was constructed to reflect the relationship between distance and receiving signal strength, which incorporates the underground complex communication characteristics, including the analytical ray wave guide model, the Shadowing effect, the tunnel size, and the penetration effect of obstacles. Secondly, a trustable anchor node selection algorithm with node movement detection is proposed, which solves the problem of high-precision node location in anisotropic networks with sparse anchor nodes after the disaster. Consequently, according to the node location and the obstacles, the optimal guidance path is developed by using the modified minimum spanning tree algorithm. Finally, the simulations in the 3D scene are conducted to verify the performance of the proposed method on the localization accuracy, guidance path effectiveness, and scalability. For underground disasters, the rescue operation is especially tricky due to the complex underground environment after a disaster occurring ,2,3. TheWhen underground disasters occur, the underground topography will be changed greatly, causing unexpected collapses and fault belts. The tremendous change of underground topography deteriorates many communication links seriously and makes the communication among nodes under more complex and various conditions. Furthermore, the disaster will result in movements of most sensor nodes, and the original location information will not be in effect . These nThe node localization methods are classified into range-based methods  and rangThere are many works on the construction of the underground channel model. In Reference , a probaTo address the obstacle effect, a semi-empirical mixing dielectric model is proposed for predicting the moist soil between sensor nodes , but theAs for the range-based localization methods for the underground environment, in Reference , a chainReference  proposesOne of the crucial problems is that the accidents will result in numerous nodes, including anchors and blind nodes, out of operation, which will bring the extreme difficulty for node localization. For improving the localization accuracy under the situation with insufficient anchors, a range-free localization algorithm called RAPS with trustable anchor pair selection for anisotropic networks is presented . SimilarThe hybrid channel model for underground scenarios is constructed, which integrates the analytical ray waveguide model, the fading effect of underground limit space, and the penetrating effect of obstacles. It can provide sufficient capacity to describe the communication characteristics of the complex underground environment, especially for disaster situations.The localization method based on anisotropic sparse anchors network after disasters is designed, which improves the accuracy of distance measurement by selecting the trustable anchors. Different sparse situations are analyzed, and the trustable anchors are chosen according to the hop count information, the distribution density of network nodes, and the theoretical minimum distance per hop.The optimal safety guided path is generated based on network node location information for the underground disaster scene. The spanning-tree based on network topology is constructed from the nodes in the trapped area to exit. All algorithms and propagation channel models are verified and tested in a 3D underground wireless sensor network scenario based on Matlab.In this paper, firstly, the geological and spatial characteristics of the underground scenarios are analyzed. Then the hybrid channel model of communication is introduced for the underground scenario, which reflects the real characteristics of underground wireless communication. Based on the hybrid channel model of a complex underground environment, the localization method based on sparse anchors is proposed for emergent accidents. In the proposed method, the anchor selection algorithm is designed to obtain the trustable anchors. Then the guidance path can be developed with fewer obstacles or shorter distances. Thus, the loss of life and personal injury can be reduced in case of an emergency. The main contributions are summarized as follows:The remainder of this paper is organized as follows—Although many nodes become invalid, the WSN previously deployed can still keep the network connectivity. The communication can be revived from the collapse area to the escape exit by node relay.There are still some nodes that do not move and can know their accuracy locations. These nodes with valid location information can be taken as anchors to locate other blind nodes. Each anchor has its known accurate position and can share its information with other nodes, including the number of hops, multi-hop paths, position information, and RSSI to sensor nodes. The specific method for determining whether the node location information is valid is described in In some areas with sparse anchor distribution after disasters, there are not enough trustable anchors in 1-hop communication. Thus, it is necessary to find trustable anchors by multi-hop communication. For the worst case, some relocated nodes will be chosen as the anchors to locate the other blind nodes.A gateway is set at each escape exit of the mine. The green square in the first picture of In this paper, if the previous deployed wireless network still keep the connections, it is feasible to develop the guidance path to accelerate the survivors escaping. The optimal escape guidance path can be designed according to the locations of nodes. However, after the disasters, many nodes will become blind nodes and need relocalization. If one node location does not change, and its saved location information is still valid, the node can be taken as the anchor to locate blind nodes. In order to achieve the objective of node localization and path developing, it is preliminary to make some assumptions for the scenario after disasters:Based on these assumptions, a node localization and path guidance method is proposed, which mainly includes four phases, as shown in A complete underground mine emergency rescue communication system is generally a mixed medium type (MMT) structure ,28, inclIn phase 1, the wireless sensor network is deployed in the underground scenario initially. Nodes are uniformly distributed, and each node has valid preset location information. The protocol for wireless sensor networks is ZigBee. In order to improve the connectivity of the network, a mesh network topology is constructed so that the network layer can search for the best escape path. In our system, there are only two types of nodes: coordinator and router. The gateway, as shown in After the disaster, numerous nodes will change their actual locations and become blind nodes. Simultaneously numerous nodes will be destroyed, and the network will reconstruction the communication connection, which is phase 2. In phase 3, the nodes with invalid location information will calculate the location with the assistance of the anchors. After re-estimating the node location, the escaping path will be developed according to the node location.Before disasters, the underground scenario has mainly consisted of laneways and a few lobbies. The laneway is a typically long and narrow wireless communication channel. Moreover, the halls can be approximated by the free-space communication model. After disasters, such as collapse, wireless sensor nodes will change in the following aspects—(1) The random number of sensor nodes will become failure and can not participate in the network activity anymore; (2) The locations of many nodes will be changed; (3) There may be some unexpected obstacles among sensor nodes and the channel characteristics will be changed. The communication condition will become more complex. Therefore, it is a critical issue of how to model the wireless channel of the underground scenario and how to simplify the model .The detection of obstacles and communication characteristics requires the consideration of the possible disaster scenarios in the underground mines. There are three kinds of typical communication modes: (1) line-of-sight propagation in the tunnel with the reflection and refraction overlapped between the two sensor nodes; (2) When a disaster occurs, the tunnel will collapse or be squeezed, there may no be the line-of-sight propagation. Thus large scale fading and multipath effect exist to decline the signal transmission between two sensor nodes; (3) In some severe disasters, the wireless channel between sensor nodes can even be completely shadowed by collapsed obstacles. The radio signal has to penetrate the obstacle to achieve communication. Under this case, the wireless channel has distinctly different characteristics from the above two cases. Thus, in this paper, a data-driven method is designed to detect the case that the wireless channel is completely shadowed by obstacles. In the following content, the data-driven method is described in detail, then these communication characteristics of three modes are analyzed individually.h is the height of the tunnel, G is the gain of the antenna.Wireless communication in underground mines is rather different from that in the air on the ground. In the underground mines, there are extensive long and narrow tunnels with various depth levels. The length and widths of the mine tunnels have an obvious impact on wireless communication. The propagation characteristics of radio waves in underground mines vary significantly on different terrestrial conditions, such as roughness of sidewalls, disorientation in orebody, curvature and bends, gallery dimension, and cross-cuts. To accurately describe the effect of the territorial space of underground mine on the propagation characteristics of radio waves, the multimode waveguide model is utilized as Equation  29,30]:,30:29,30Prxld=10rived in  as:(3)CLWhen the signal is transmitted in the underground laneway, the reflection and refraction will occur due to the influence of the underground scenario wall and obstacles, which cause serious signal decay. Under this case, it is difficult and imprecise to analyze the attenuation effect of the practical environment factors and to construct the theoretical model based on the features of electromagnetic waves. Therefore, constructing the empirical model is usually an effective method. The statistical characteristics are summarized rather than analyzing the mechanism of the actual communication. According to the principle of minimum error, the mathematical relation forms of the parameters and variables are concluded. The empirical model only considers the relationship between input, output, and parameters, which is independent of communication mechanisms.jth Tunnel transfer function at frequency d. N is the number of sweep point in every transfer function and M is the number of measurements at each point. The detail calculations of above parameters can be referred to [Numerous conducted experiments indicate that the mean path loss increases exponentially with distance. That is, the mean path loss is a function of distance. Thus, it is more visual to plot the power-distance relationship on the log-log scale, by which the relationship is a straight line. Absolute mean path loss, in decibels, is defined as the path loss from the transmitter to the reference distance model is :(4)ld=l0et model  is introerred to .When some collapse occurs in the underground environment, there will be obstacles between sensor nodes, and communication links between sensor nodes will be blocked. Because the Tunnel of the underground laneway and multi-floor Tunnel have similar characteristics, the model of path loss can be established by considering the space loss of Tunnel boundary absorption :(6)Prx=Pd is the distance between the transmitting device and the receiving device. C is the threshold value of the number of signals that pass-through obstacles. Like the above model, there is :(7)ld=PtTaking  into 6)6), it isAfter a radio signal radiates into the air through the antenna, its intensity will be attenuated due to the negative effects of absorption, transmission distance, and multi-path phenomenon. Different materials often produce different decay results. The 2.4 GHz signal drops 3 dB after penetrating the drywall, meaning that the amplitude decreases double. The 2.4 GHz signal penetrating the brick wall will decrease by 12 dB, which means the amplitude is 16 times lower than the original amplitude. Water and dense materials, including cinder bricks, are the main causes of radio absorption, all of which lead to signal decay greatly.When some collapse occurs in the underground environment, there will be obstacles between sensor nodes, and communication paths between sensor nodes will be blocked. In an indoor environment, only a single sampling and processing of the RSS of a sensor node cannot determine whether there are obstacles in the link between the nodes. However, with the receiver and transmitter positions determined, We can build a data set containing a large number of RSS samples. By extracting the feature of the data set and analyzing it, it is possible to construct a data-driven model to detect the presence of obstacles between links.In the indoor environment, we set a pair of sensor nodes in the room and corridor, respectively, and use doors to simulate obstacles. Closing the door simulates the presence of obstacles between links. The transmitting node sends data in a cycle of 0.025 s, and the receiving node completes the sampling of the RSS samples once, and 1000 samples create a sample group. For every 100 sample groups collected, the location of the receiving node is changed and recorded. The final sample set is 1600 groups (800 groups with obstacles). All RSS samples are randomly divided into 5 Sample subsets for cross-validation. Considering the influence of the antenna direction on the experimental results, during the experiment, the transmitting node and the receiving node still ensure that the antennas are opposite and parallel even after redeployment.In this paper, a link obstacle detection algorithm based on hypothesis testing classifier (HTC) is adopted and compared with the support vector machine method. First, feature extraction is performed on the RSS samples. The extracted features include the mean, the skewness, and the Rician K Factor. t is asCompared with the Support Vector Machine (SVM) method, the HTC algorithm has slightly lower accuracy, but the training time is significantly reduced. Therefore, the HTC algorithm is more suitable for mobile computing and embedded systems. In order to achieve the obstacle detection between links, the likelihood ratio detection, including two competing hypotheses (with obstacles/without obstacles), is defined as  t is ast=p(x(1),The performance of the HTC algorithm is evaluated in terms of missed detection probability (deciding without obstacles when the RSS samples are from with obstacles conditions) and false alarm probability (deciding with obstacles when the RSS samples are from without obstacles conditions). The overall detection error probability is the sum of the above two probabilities. The experimental results are shown in In summary, if a sample group contains 1000 samples and the frame sending period of the sensor node is 25 ms, the detection of whether the link is blocked by obstacles can be completed in 25 s, and the detection error is less than In the practical underground environment, especially after disaster occurs, the communication situation is complex. There may exist some adverse effects such as multipath effects, shadow effects, and obstacle blocking. Therefore, the unique model can not sufficiently describe the propagation characteristics of wireless signals in an underground mine environment.This view was also confirmed by wireless signal propagation measurement experiments performed in a tunnel environment. The experiment uses the TelosB mote. During the experiment, the antenna of TelosB is kept as a vertical polarization pattern, and the gain is 2 dB. The experimental results are shown in In the initial assumption, every node has the accurate location information when deploying. In the case of the disaster, the nodes will deviate away from their initial location. First, the HTC algorithm described in B = C and B = C otherwise. However, the original locations can provide a rough estimation of path loss between nodes. If let By utilizing the hybrid channel model, the distance between nodes and anchors can be measured. The hybrid channel model sufficiently describes the complex underground communication features. The sensor node senses the transmission losses of the closest anchors. Then judges the piecewise conditions of the hybrid model. Based on it, the model parameters are fitting by the attenuation or penetration of wireless signals. After determining parameters, the distance between the sensor node and the corresponding anchor can be calculated, which is necessary for the node locating and guidance path developing.This paper focused on developing an escape guidance path for underground survivors. The fundamental requirement of the guidance path is that the path can make the detour for obstacles with the shortest length. Obstacle avoidance can be achieved by utilizing the hybrid channel model to estimate the obstacle existence. The developing path with the shortest length should recur to the location of nodes. Just using the node location information, the node can pick out the node within 1-hop closest to the escape exit. However, after disasters, numerous node locations will change, and the initial location information will be invalid. In some cases, there are not sufficient anchors which still have location information. This brings the challenge for node relocalization. This section discusses node localization and guidance path method under the anisotropic sparse anchors network after disasters.In this paper, the method of wall contactor and accelerometer is used to judge the movement of nodes. Compared with the method of the accelerometer, the wall contactor method is simpler and lower cost, but the scalability and detection accuracy are relatively low.The ground contactor is a contact switch. As shown in As shown in SMV is only affected by the acceleration of gravity. When a sensor node falls due to a disaster, the node is in a weightless state before landing, and the SMV value tends to 0. At the moment of landing, the node receives a shock, and the SMV value will increase instantly. By detecting this change process of the SMV, it can be judged whether the position of the sensor node changes and becomes a blind node.In the normal state, the sensor node is at rest, and the In the wireless sensor network, if there are sufficient anchors within the 1-hop range of a blind node, the trilateration localization algorithm can be exerted directly to estimate the position of the blind node with the range-based method. However, for underground scenarios after the disaster, because lots of sensor nodes become blind or invalid, there are not sufficient 1-hop anchors. So the remote anchors should be selected to participate in the localization, but for a blind node, not all remote anchor nodes are suitable localization reference nodes. These suitable positioning reference nodes are defined as trustable anchor nodes. In the following, the trustable anchor selection algorithm will be presented by analyzing different sparse cases.As shown in X and the remote anchor A h=1,In this paper, the shortest possible minimum distance between blind nodes and anchors X, and the distance between the trustable anchor X, X is located on the spherical face When there is just one trustable anchor in the neighbor set of the blind node, the shortest possible distance between the blind node and the possible anchor is determined, as shown in X, the point, denoted by X.To determine whether the possible anchor X. According to the hybrid model in the XX is on the circle C where the spherical face C and anchor node X.When there are two trustable anchors in the neighbor set of the blind node, the situation is shown in X. According to the hybrid model in the XX is one of the two intersections of the spherical face X to the possible anchor X.When there are three trustable anchors in the neighbor set of the blind node, the situation is shown in In the 3D space scenario discussed in this paper, the trustable anchor node and its coordinates are denoted as quations .(15)d12However, in a real wireless sensor network, whether using the range-based or range-free method, there is a deviation between the estimated distance between the nodes from the actual distance. The least-square method is an effective method to solve this problem. After decomposing and recombining the Equation , the newVectors quations –20), re, reX andx, y, z) of the anchor node x can be obtained. The simulations show that the greater the number of trustable anchor nodes, the higher the accuracy of blind node position estimation.Using the least-squares algorithm and solving the matrix equation to get the vector When developing the escape path, generally, the shortest path can let the trapped people escape as quickly as possible. Thus, the algorithm is proposed to obtain the path with the shortest distance to the exit in all possible paths. The proposed algorithm is based on the minimal spanning tree which can find the path with the shortest distance between any two connected end nodes, called the source node Similar to , the algFirstly, the last hop nodes are found backward from the target node Algorithm 1 Algorithm description of escape guidance path developingInput: 1: Output: 2: Initialize: h = 1, node i = target node, node u = target node3: while (node i != source node) do4: if node i has next trustable anchor node nthen5:  d between node i and node n6:   calculate the distance 7:   add the path h = h + 18:   u = node i, node i = node n9:   node else10:  i = node u11:    node end if12:  end while13: forj = 1 to hdo14: d in 15:  sum all 16:  choose the shortest end for17: return18: As shown in Algorithm 1, after researching all possible paths and calculating all path lengths, the shortest distance path can be selected. The shortest distance path can be recommended as the optimal escape paths. The computing complexity of Algorithm 1 is similar to the conventional minimal spanning tree constructing algorithm, so it is O is taken as the evaluation index, whose definition is Equation . Where tThe three algorithms RAL , DV-distThe algorithm RAL proposes an accurate average hop-length estimation algorithm. The algorithm can both tolerate irregular radio propagation and the distortion effect of obstacles, thus improving the node localization performance in anisotropic wireless sensor networks.The algorithm DV-distance proposes an assistant anchor localization strategy to solve the problem of low node localization accuracy in irregular (anisotropic) wireless sensor networks.The algorithm CVLR solves the LAEP’s hop-distance ambiguity problem based on position correction vectors, thereby improving the localization accuracy of blind nodes.To show the scalability of the proposed method, From R. While for CVLR, there are just 50% that can reach the localization accuracy. As for RAL and DV-distance, the two methods have lower localization accuracy. The results validate our proposed method with more details.To further present the performance of these methods, By using the proposed method, the node localization and guidance path results are illustrated in In this paper, a localization and guidance method is proposed for the wireless sensor network in the underground scenario. The core objective of the method is to generate the optimal escape path for underground trapped survivals in the case of emergency, even in which a collapse has occurred. The proposed method can accelerate the escape procedure and improve the survival probability. On the other hand, the proposed method can provide the rescue-assistance function to ground rescue works by presenting the optimal rescue route. Considering the complex communication environment in the underground scenario, the hybrid channel model is constructed to incorporate the diversity attenuation or penetration characteristics of the wireless signal, such as the fading, multipath, and blocked effect. Then the proposed method can choose the most proper anchor to improve the node localization accuracy. After node localization, the guidance path is generated with the shortest distance and fewer obstacles. The simulation results show that our proposed method can achieve improved localization accuracy and provide the proper guidance path to escape. In further research, the judgment threshold of RSS will be dynamically adjusted to improve the adaptability to the environment change. In the practical application of wireless sensor networks, there may be hidden nodes in the network. Future research will study the influence of such nodes on network localization to improve localization accuracy.