Snail Trail Research Articles

Highly stable peptide adducts from hard keratins as biomarkers to verify local sulfur mustard exposure of hair by high-resolution mass spectrometry

In the recent past, the blister agent sulfur mustard (SM) deployed by the terroristic group Islamic State has caused a huge number of civilian and military casualties in armed conflicts in the Middle East. The vaporized or aerolized agent might be inhaled and have direct contact to skin and hair. Reaction products of SM with plasma proteins (adducts) represent well-established systemic targets for the bioanalytical verification of exposure. The SM-derived hydroxyethylthioethyl (HETE)-moiety is attached to nucleophilic amino acid side chains and allows unambiguous adduct detection. For shipping of common blood and plasma samples, extensive packaging rules are to be followed as these matrices are considered as potentially infectious material. In contrast, hair is considered as non-infectious thus making its handling and transportation much less complicated. Therefore, we addressed this matrix to develop a procedure for bioanalytical verification. Following optimized lysis of SM-treated human scalp hair and pepsin-catalyzed proteolysis of adducts of keratin type I and II, microbore liquid chromatography–electrospray ionization high-resolution tandem-mass spectrometry (µLC–ESI MS/HR MS) was used to detect three alkylated keratin-derived biomarker peptides: AE(-HETE)IRSDL, FKTIE(-HETE)EL, and LE(-HETE)TKLQF simultaneously. All bear the HETE-moiety bound to a glutamic acid residue. Protein adducts were stable for at least 14 weeks at ambient temperature and contact to air, and were not affected by washing the hair with shampoo. The biomarker peptides were also obtained from beard, armpit, abdominal, and pubic hair. This is the first report introducing stable local peptide adduct biomarkers from hair, that is easily accessible by a non-invasive sampling process.

Prolonged degradation and reliability assessment of installed modules operational for 10 years in 5 MW PV plant in hot semi-arid climate

The current work presents detailed degradation and reliability assessment of installed PV modules in grid tied 5 MW solar PV plant in hot semi-arid climate. Defects and failures of 336 modules out of 2078 visually inspected c-Si modules with 10 years of operational period in hot semi-arid climate is evaluated using FMECA statistical tool and I-V curve measurement. The novelty of the research conducted is presenting long term degradation of PV modules in utility scale PV plant installed for 10 years in outdoor environment in remote location, carrying FMECA analysis of visually degraded modules along with on-field electrical characterization. Correlation of identified defect and failures with electrical parameters of PV module is also presented. The results obtained indicate light and dark EVA discoloration, EVA discoloration with cell and metallization discoloration, snail trails, backsheet burnt, glass breakage with or without cell cracks to be the most dominant and frequently occurring defects and failures with high severity. The inspected plant has considerable degradation as compared to other plants in the same area in hot semi-arid climate and therefore degradation investigation of the plant is important in identifying various possible causes of observed degradation and studying impact of degradation on module performance. Based on the results obtained and analysis conducted, several important preventive and corrective measures along with important recommendations have been enlisted for mitigating failures and improving the system performance.

Machine vision based fault diagnosis of photovoltaic modules using lazy learning approach

Machine Vision is an advanced and powerful imaging based technique that has been applied in various fields like robotics, inspection and process control. Machine vision, in industrial terms, is termed as a subcategory of computer vision. The primary aim of the proposed study is to distinguish various visual fault conditions that hinder the performance and life span of photovoltaic (PV) modules using computer vision and a machine learning approach. Literatures state that thermographic and electroluminescence images were used in deep learning for identifying faults in photovoltaic modules. However, the effectiveness of normal RGB images with the fusion of deep learning and machine learning techniques is less explored. This paper deals with the classification of normal RGB images of PV modules acquired using a fusion of deep learning and machine learning techniques. Visual faults like delamination, snail trail, burn marks, glass breakage and discoloration that occur in a photovoltaic module (PVM) were considered in the study. A machine learning approach was used to handle this problem which contains three stages: (i) feature extraction, (ii) feature selection and (iii) feature classification. Initially, the features from the aerial images of PVM (acquired from unmanned aerial vehicles (UAVs) equipped with digital cameras) were extracted using convolutional neural networks (CNN). Secondly, J48 decision tree algorithm was employed to select the features of utmost significance and dominance from the extracted image features. Finally, a set of lazy classifiers like locally weighted learning (LWL), K-star algorithm (KS), nearest neighbor (NN) and k-nearest neighbor (kNN) were adopted to execute the classification task on the selected image features. The classification accuracies of all the aforementioned classifiers were compared and it was found that the k-nearest neighbor classifier achieved a maximum accuracy of 98.95% with a lesser computational time of 0.04 s.

Contraceptive effect on ovarian cysts

A fluid-filled sac inside the ovary is known as an ovarian cyst. Frequently, they have no symptoms. They occasionally cause bloating and may also cause lower back discomfort or lower abdomen pain. Cysts are often not harmful. By causing synthetic steroids to operate negatively on the hypothalamic-pituitary axis, oral contraceptives suppress ovarian activity and ovulation. Cross sectional study was conducted on ovarian cyst patients in Tikrit City in Salah-Eddin province from January to June 2021. A total of 70 women with ovarian cyst disorder participated in this study (all were married) and their age range was from 20 to 40 years old. 50 healthy women were also taken as a control group. Questionnaires were design to ask about age, residence, duration of illness, precipitating factors or aggravating factors, medical history & type of treatment were studied. The most important symptoms affecting ovarian cyst patients are abdominal pain, acnes, nausea, breast pain, irregular period, depression, headache, body hair and dysuria. Most patients were using contraceptives and most of them were using the pill contraceptives. The most important symptoms affecting ovarian cyst patients are abdominal pain, acnes, nausea, breast pain, irregular period, depression, headache, body hair and dysuria.

The Absorption, Distribution, and Excretion of 18 Elements of Tibetan Medicine Qishiwei Zhenzhu Pills in Rats with Cerebral Ischemia.

The aim of this study is to determine 18 elements in Tibetan medicine Qishiwei Zhenzhu pills (QSW) and their absorption, distribution, and excretion in rats with cerebral ischemia. Microwave digestion and inductively coupled plasma mass spectrometry (ICP-MS) were used to determine 18 elements of QSW in simulated gastrointestinal (GI) juice. Rats were given QSW (66.68 mg/kg) followed by middle cerebral artery occlusion (MCAO). Sham rats received saline and were not subjected to MCAO. ICP-MS was applied to determine the content of 18 elements in hepatic venous blood, abdominal aortic blood, brain, liver, kidney, hair, urine, and feces 24 h after MCAO. In vitro results showed that the extraction rate of Mn, Cu, Sr, Pb, Au, and Hg of QSW in gastric juice (1 h) was higher than that in water, and the contents of Cu, Au, Sr, and As were higher in intestinal juice (4 h) than in water. In vivo results showed that the contents of elements in the blood were quite low, and QSW increased Ni, Cr, Sr, Co, and V in artery blood and decreased V in venous blood. Elements in the tissues were also low, and QSW increased brain Li but decreased Cr and Cd; QSW increased kidney Ag and Cs and liver Mn but decreased liver Ni. QSW increased urinary excretion of Li, Sr, Hg, Cs, and V; QSW increased Hg content in hair but decreased Ni. Stool is the main excretion pathway of the elements in QSW, with Ba, Mn, Sr, Cd, V, Cu, Cs, Li, Pb, Ag, Hg, Cr, As, and Co the highest. In summary, this study examined the distribution of 18 elements in QSW-treated MCAO rats. The accumulation of these elements in blood and tissues was extremely low, and the majority was excreted in feces within 24 h, highlighting the importance of the gut-microbiota-brain axis in QSW-mediated brain protection.

Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features

ABSTRACT Visual faults in photovoltaic (PV) modules persist as a problem that can create consequences such as reduced life span, increased output power loss and raising safety concerns during operation. Increased manpower requirement, larger time consumption, confinement to single fault prediction and high initial cost are certain drawbacks of conventional fault diagnosis techniques. Recent advancements in technology and the innovation of scientific techniques have urged the need for advanced fault diagnosis techniques that deliver instantaneous results. In the present study, unmanned aerial vehicles (UAVs) were employed to capture images of PVM with visual faults. The most common visual faults in photovoltaic modules (PVM) are delamination, burn marks, glass breakage, discoloration, and snail trails. Each fault condition contains a unique image pattern appearance attributed to the particular type of fault. Such patterns are extracted using convolutional neural networks and classified with the help of decision tree algorithms. First, the features are extracted from these aerial images by utilizing pre-trained AlexNet convolutional neural networks. Secondly, the J48 decision tree algorithm is utilized to select the most significant and valuable features from the extracted image features. Finally, the classification is carried out with several decision tree algorithms such as decision stump, hoeffiding tree, J48, linear model tree (LMT), random forest, random tree, representative (REP) tree, best first (BF) tree, extra tree, functional tree (FT), J48 consolidated, J48 graft, least absolute deviation (LAD) tree, naïve bayes (NB) tree and simple cart. The classification accuracies of the algorithms mentioned above are compared to suggest the best-in-class algorithm for real-time application. Among all the available tree-based algorithms, the random forest algorithm produced a maximum classification accuracy of 98.25% with a computational time of 0.89 seconds.

Photoluminescence for Defect Detection on Full-Sized Photovoltaic Modules

Cost-effective, fast, and nondestructive on-site characterization of photovoltaic plants is required to determine countermeasures against power loss, defects, or safety problems. Methods with small impact on the operation and a high throughput, such as infrared thermography (IR), or methods with high resolution for detailed defect information, such as electroluminescence (EL) imaging, are expedient. To combine high resolution and high throughput, we propose to use photoluminescence (PL) as an outdoor characterization method for full-sized module imaging. With PL imaging as with IR imaging, no electrical contact is necessary, yet image resolution is on par with EL images. Our outdoor PL setup features an excitation source with 18 broadband, white, high power, chip on board LEDs coupled with low-cost short pass filters. This setup is suitable for indium gallium arsenide and silicon detectors. Here, we compare the visibility of common defects, including short-circuited bypass diodes, cracks, potential induced degradation, snail trails, ethylene-vinyl acetate degradation, and interconnection failures, in PL images to that in state-of-the-art imaging techniques IR, EL, and ultraviolet fluorescence. We find that out of these seven defects, five can be detected well, cell cracks under certain conditions, and interconnection failures not at all. We discuss how different techniques are complementary to enable better defect detection.

Analysis of the impact of power loss due to snail trails in a 95-kWp photovoltaic power system

Snail trails are a prevalent degradation phenomenon observed in solar power plants. In a previous study, it was determined that snail trails affected only the appearance of solar cells, without significantly contributing to actual power loss. However, when snail trails are accompanied by solar cell cracks, power loss occurs. We analyzed the snail trails in a specific module in a 95 kW power plant that has been operational since 2015. Moreover, we evaluated the power generation loss arising from snail trails using current–voltage measurements and electroluminescence analysis.

The protein and volatile components of trail mucus in the Common Garden Snail, Cornu aspersum.

The Common or Brown Garden Snail, Cornu aspersum, is an invasive land snail that has successfully colonized a diverse range of global environments. Like other invasive land snails, it is a significant pest of a variety of agricultural crops, including citrus, grapes and canola. Cornu aspersum secretes a mucus trail when mobile that facilitates locomotion. The involvement of the trail in conspecific chemical communication has also been postulated. Our study found that anterior tentacle contact with conspecific mucus elicited a significant increase in heart rate from 46.9 to 51 beats per minute. In order to gain a better understanding of the constituents of the trail mucus and the role it may play in snail communication, the protein and volatile components of mucus trails were investigated. Using two different protein extraction methods, mass spectrometry analysis yielded 175 different proteins, 29 of which had no significant similarity to any entries in the non-redundant protein sequence database. Of the mucus proteins, 22 contain features consistent with secreted proteins, including a perlucin-like protein. The eight most abundant volatiles detected using gas chromatography were recorded (including propanoic acid and limonene) and their potential role as putative pheromones are discussed. In summary, this study has provided an avenue for further research pertaining to the role of trail mucus in snail communication and provides a useful repository for land snail trail mucus components. This may be utilized for further research regarding snail attraction and dispersal, which may be applied in the fields of agriculture, ecology and human health.

A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration, glass breakage, and snail trails make detection of faults difficult under harsh environmental conditions. Various researchers have made several attempts to identify visual faults in a PVM. However, much of the previous studies were centered on the identification and analysis of limited number of faults. This article presents the use of a deep convolutional neural network (CNN) to extract image features and perform an effective classification of faults by machine learning (ML) algorithms. In contrast to the present-day work, five different fault conditions were considered in the study. The proposed solution consists of three phases, to effectively analyze various PVM defects. First, the module images are acquired using unmanned aerial vehicles (UAVs) and data augmentation is performed to generate a uniform dataset. Afterward, a pre-trained deep CNN is adopted for image feature extraction. Finally, the extracted image features are classified with the help of various ML classifiers. The final results show the effectiveness of pre-trained deep CNN and accurate performance of ML classifiers. The best-in-class ML classifier for multiple fault classification is suggested based on the performance comparison.

The Story of a Radiologically Undetectable, Testosterone Producing Leydig Cell Tumor

Introduction: Leydig stromal cell tumors are uncommon ovarian tumors that produce testosterone leading to hyperandrogenism. We present a case of a 63 year old lady with significantly elevated testosterone levels that did not have clear ovaries visualized on imaging, but was subsequently found to have a Leydig cell tumor on pathology after ovarian resection.Clinical Case: A 63 year old female with a past medical history of COPD, hypothyroidism, hyperlipidemia, hypertension and uterine fibroids status post hysterectomy and left oophorectomy in 1995 was referred to endocrinology for hirsutism. The patient reported first noticing abnormal hair growth approximately one year prior to presentation having developed increasingly coarse and thick facial hair, abdominal wall hair, and chest hair. On physical examination, she was noted to have coarse hair across her upper lip and chin continuous along the jawline along with fine, dark hair diffusely across her anterior abdomen. Initial laboratory cell evaluation revealed total testosterone 378 ng/dL, free testosterone 53ng/dL, DHEAS 64 ug/dL. Repeat labs drawn three months later confirmed the markedly elevated total testosterone of 362 ng/dL and free testosterone 44.2 ng/dL, concentrations normally seen in males. A CT scan of the abdomen and pelvis was done and did not reveal any masses. In addition, no ovaries were appreciated on imaging. A transvaginal ultrasound also did not reveal any clear ovaries. The patient ended up undergoing a right oophorectomy. Histological examination was consistent with a Leydig cell tumor. Following oophorectomy, her testosterone concentrations normalized (5ng/dL) and hirsutism began to regress.Clinical Lesson: Hyperandrogenism in women is typically classified into non-tumorous and tumorous. The differential for non-tumorous hyperandrogenism includes PCOS, congenital adrenal hyperplasia (CAH), and ovarian hyperthecosis. Tumorous causes include ovarian tumors such as Sertoli-Leydig cell tumors, hilus cell tumors, and theca cell tumors. Adrenal tumors secreting testosterone are extremely rare. Often with these tumors, there is significantly increased testosterone levels (> 140ng/dL) and rapid progression of symptoms. Sex cord stromal tumors account for only 5-8% of all ovarian tumors with Leydig stromal tumors a rare group that accounts for less than 0.1% of all ovarian tumors. The tumors are functional producing testosterone leading to marked hyperandrogenism and virilization. They are also usually benign and unilateral. This patient had hyperandrogenism manifested by hirsutism with markedly elevated testosterone concentrations. In addition, this case is unique in that a CT scan and transvaginal ultrasound did not clearly demonstrate her right ovary. Despite having a normal appearing right ovary during surgery, patient was found to have Leydig cell tumor following histological examination.

Visual degradation and performance evaluation of utility scale solar photovoltaic power plant in hot and dry climate in western India

The current work presents techno-economic study, degradation analysis and performance assessment for seven consecutive years from 2013 to 2019 of 5 MW grid connected crystalline silicon solar photovoltaic (PV) power plant with monthly variation in tilt adjustment done manually in hot and dry climate of Gujarat, India. Hot spots, junction box melting, encapsulant discoloration, snail trails, corrosion of metallization and interconnects due to moisture ingression were among the main degradation modes and faults observed in large number of fielded PV modules with operational time of eight years. Various performance indices such as monthly mean reference yield, final yield, performance ratio (PR), capacity utilization factor (CUF) and PV system efficiency are calculated from recorded energy generation and environmental data and analyzed. Economic and Environmental analysis of the inspected plant in terms of payback period, levelized cost of electricity (LCOE) and mitigated CO2 emission is also presented. PVSyst and Helioscope are the software simulation tools used for PV plant modeling, losses prediction and verification with the calculated performance indices. Possible causes, impact and corrective measures for various degradation mechanisms and failures observed in fielded PV modules are presented. Based on the findings by number of researchers globally and conducted performance and degradation analysis of the inspected plant, specific conclusion and key recommendations are enlisted.

Fault Detection in aerial images of photovoltaic modules based on Deep learning

Operation and maintenance of photovoltaic (PV) modules are currently the prime concerns of the expanding photovoltaic industry. Unmanned aerial vehicles (UAVs) are applied in the field of inspection and monitoring of faults that occur in a photovoltaic module (PVM). Such inspections can significantly reduce time and human interference to provide accurate classification results. Technological advancements and innovative techniques in the fast moving world expect instantaneous results. Fault diagnosis is one such technique that provides instantaneous results and assures enhanced lifetime of various critical components. This paper presents the fault detection in PVM based on deep learning with the help of aerial images acquired from UAVs. Convolutional neural networks (CNN) are adopted to extract high level features from the images which are classified using the softmax activation function. The feature extraction and fault classification is carried out by using a pre-trained VGG16 network. A total of six test conditions are considered in the study. Burn marks, delamination, discoloration, glass breakage, good panel and snail trail are the several test conditions considered. The classification result for the pre-trained CNN model is exhibited and performance of the model is evaluated.

Risks of work with cytotoxic drugs

Background. Cytotoxic drugs (CTD) are widely used in medicine for the treatment of tumors and other diseases. According to the definition, CTD are the drugs, which are toxic for cells, that is, interrupt their replication or growth. Effects of CTD are not targeted, that’s why when they get into the organism, they can harm not only target cells, but any other cells, and not only the patient, but also the other people who contact with them, especially, the healthcare workers (HCW).
 Objective. To define the risks of work with CTD.
 Materials and methods. Analysis of the literature data on this topic.
 Results and discussion. In case of breaking the safety rules, HCW may contact CTD. They can be exposed via skin contact, inhalation of aerosols or swallowing of the particles of CTD, via needle injuries. Actions and procedures, which can be accompanied by the contact of HCW with CTD, include drug preparation and administration, handling of drug containers and waste materials, handling the biological liquids of CTD-treated patient, cleaning of the spills or items, polluted by biological liquids. Exposure to CTD can result in the abdominal pain, hair loss, vomiting, liver injury, nasal sores, contact dermatitis and allergic reactions, miscarriages and fetal malformations (in pregnant women), changes in total blood analysis, mutations appearance. It is not proven that long occupational exposure to CTD leads to cancer, despite it is known that the majority of CTD have genotoxic, cancerogenic and mutagenic effects. Tactics of risk minimization in work with CTD includes definition of risk groups (pregnant women, newbies), assessment of the potential harms of used drugs, written fixation and often reassessment of risk estimation, HCW education. The latter must include methods of CTD preparation, peculiarities of CTD usage and waste handling, correct usage of individual protective equipment (IPE) etc. CTD must be prepared exclusively by the HCW, who underwent the adequate training, and in the specialized settings (HEPA-filtered exhaust systems, negative pressure rooms, absorbing surface covers). IPE must include vinyl or nitrile gloves, waterproof long-sleeved robe or overall with elastic cuffs, goggles or face shield, respirator, shoe covers. Potentially polluted by CTD waste must be collected into the polypropylene or polyethylene bags with the appropriate labeling.
 Conclusions. 1. CTD may harm not only target cells, but also the other cells, and not only patients, but all the other exposed people. 2. CTD exposure may lead to the abdominal pain, hair loss, vomiting, contact dermatitis, miscarriages and fetal malformations, etc. 3. There is a spectrum of protective measures to protect HCW from CTD exposure (IPE, equipment of specialized working places for CTD solutions’ preparation, correct waste handling).

Analysis of the Impact of Power Loss by Snail Trail Degradation in Solar Power Plant

Analysis of the Impact of Power Loss by Snail Trail Degradation in Solar Power Plant

A preliminary study of common defects of photovoltaic modules in West Timor, Indonesia

Photovoltaic (PV) modules deployed outdoor are exposed to various environmental conditions which can contribute to degrade their power output. Visual inspection is well known as an initial method commonly applied to assess degradation modes of PV modules. By applying the method, it was found that the modules installed on eight off-grid solar power systems in West Timor, Indonesia experienced several degradation modes such as delamination, discoloration, snail trails, microcracks, corrosion, and soiling. These types of degradation are likely attributed to the high temperature, humidity, and salinity level in the region. The degraded panels are still operating to provide electricity for the community in these areas. However, it can be predicted that the energy produced is not optimal as healthy PV modules. Further research needs to be carried out to assess relationship of the degradation modes and their effect to the value of electrical parameters of the modules.

Diagnosis of Photovoltaic (PV) Panel Defects Based on Testing and Evaluation of Thermal Image

Abstract Photovoltaic (PV) solar energy can only be economical if the PV module operates reliably for 25–30 years under field conditions. The PV module and it overall reliability can be radically affected by faults during the manufacturing process, in real field conditions, transportation, and installation. So, there is a need for diagnosing defects in PV modules to improve their reliability. Operating temperature plays the key role for improving the efficiency of PV panels. The temperature within the PV cell unevenly increases because of such defects in the cell. As such, it is very important to monitor the temperature and temperature distribution in PV panels in order to locate such defects. Infrared thermography (IRT) plays a major role in predictive and preventive maintenance of PV panels and can determine the severity of the problem. This article investigates the delamination, snail trails, and bubbled faults of PV panels using digital thermal image analysis and their feature extraction. Real time experiments were conducted, and the test results are presented in this article. Thermal images of panels are captured using a (FLIR) T420bx® thermal imager. The thermal images of panels are analyzed by segmenting the image using the k-means clustering algorithm. Histogram statistical features such as mean, standard deviation, variance, entropy, skew, and kurtosis are extracted from the segmented thermal image. Based on these features, the defects in PV panels are identified with reasonable accuracy.

Techno-Economic Analysis and Performance Evaluation of 25 MW Solar PV Power Plant in Actual Environmental Condition in India

This paper presents study, identification and evaluation of causes and impact of various degradation modes and environmental conditions on performance of a utility scale grid connected solar PV plant located in remote location in India. Degradation of solar PV modules results in considerable loss in energy yield of overall estimated plant generation. The research includes degradation analysis of 25 MW Roha Dyechem amorphous Si solar PV plant, Charanka, Patan, Gujarat under varying climatic conditions. Some of the well qualified modules were found to degrade in outdoor exposure for more than 7 years. Glass breakage, hot spots, backsheet puncture, micro-delamination, corrosion of cell edges, snail trails, Digital Process Control Board (DPCB) failure, moisture ingression, soiling losses etc. were among the main faults observed in fielded PV modules. A comparative analysis is presented between the simulated, computed and practically measured and recorded field data for drawing important conclusions.

The Westerns and War Films of John Ford by Sue Matheson (review)

Reviewed by: The Westerns and War Films of John Ford by Sue Matheson Robert Weldon Whalen Sue Matheson. The Westerns and War Films of John Ford. New York: Rowman and Littlefield, 2016. Acknowledgments, introduction, aftermath, illustrations, notes, index. Pp. xxi; 339. $45.00, cloth. Think of the famous filmmaker John Ford and you'll probably think of the famous actor, John Wayne. Think of John Ford and John Wayne and you might imagine a triumphalist, violent, strutting Americana, and you wouldn't be entirely wrong. Sue Matheson's The Westerns and War Films of John Ford would show you, though, that your opinion was awfully incomplete. This exhaustively researched and elegantly written study convincingly argues that when it comes to John Ford and his westerns and war films, what meets the eye is much more than you might think. A professor at the University College of the North in Manitoba and an accomplished film scholar, Matheson is the author of, among other works, Love in Western Film and Television: Lonely Hearts and Happy Trails (2013) and A Fistful of Icons: Essays on Frontier Fixtures of the American Western (2017). The Westerns and War Films of John Ford is part of Rowman and Littlefield's "Film and History" series. Matheson organizes The Westerns and War Films of John Ford chronologically. She begins, in Chapter 1, with a brief overview of Ford's long career. Each of the remaining thirteen chapters considers selected Ford films including, for example, early works like Straight Shooting (1917) and The Iron Horse (1924); Ford's World War II films; the "Cavalry Trilogy" –Fort Apache, She Wore a Yellow Ribbon, and Rio Grande (1947-1950); The Searchers (1956) and The Horse Soldiers (1959); and finally, The Man Who Shot Liberty Valence (1962) and Cheyenne Autumn (1964). Matheson knows Ford and Fordiana well. Ford is, of course, enormously influential; as Matheson notes, "[Steven] Spielberg still regards Ford as his mentor and watches Ford's films before making his own" (xvii). Matheson's thesis is that there is nothing simple about Ford's work. Matheson approvingly cites Scott Eyman's judgment that Ford was a "mass of contradiction" (xiii), but as Matheson skillfully demonstrates, these many contradictions do not cancel each other out, but rather enrich Ford's immense body of work. Ford was a photographer at heart; film for him was a photographic art and each image in his films is not only carefully composed but also evokes a wide range of visual associations. The Battle of Midway (1942), his documentary about the epic naval battle, includes sweeping images of cloud and sky, echoing the nature mysticism of the nineteenth century Hudson River School of landscape painters (101). Images from his "Cavalry Trilogy" are shaped by Civil War photographs. His earliest films reference Renaissance art. As Matheson notes, Ford had a "painterly eye" (30). At the same time, Ford was a storyteller, a narrative artist who turned as often to books as to paintings when composing a film (xvii). The child of Irish immigrants, Ford became, Matheson writes, "a seanchaí, a traditional Irish storyteller" (xv). He thought that there was such a thing as an "American character" (227) and his great goal was to show, and tell, its story. That story was complicated. Ford was a Victorian and Matheson agrees with Gaylyn Studlar that "Victorian sentiment informs what have come to be regarded as Fordian values" (xvii). Victorian sometimes means "hierarchical" and "oppressive" but such characteristics aren't typical of Ford. He did not make simple-minded "he-man" pictures (11); as prosperous as he became, he never forgot that he was the child of immigrants. Matheson writes: "Always sympathetic to the underdog, Ford adopted the stance of the outsider" (xiv) both in his films and in his personal life. Racism was, of course, pervasive in Ford's America, which makes Sergeant Rutledge (1960) all the [End Page 104] more surprising. A murder mystery set in Ford's beloved west, the film tells the story of an African American sergeant (Woody Strode) who is wrongly accused of rape and murder–a remarkable film for such a Victorian and patriotic filmmaker. John Ford was a "greatest generation" patriot. In...

Особенности субъективных проявлений, стадийность неоплазии половых органов в постменопаузальный период

The objective: to determine the structure, the stage of detecting ,the severity of subjective manifestations in the presence of tumor processes of the reproductive organs. Materials and methods. A retrospective analysis of case histories of 301 patients with tumors of the reproductive organs in the postmenopausal period was carried out. The average age of patients was 61.6±0.4 years. The duration of the postmenopausal period ranged from 11.0±0.6 years. Results. It was established that 28.9% of postmenopausal women have uterine fibroids, 29.4% have endometrial hyperplasia, 21.6% have endometrial cancer, 10.6% – ovarian cancer, 5.6% – benign ovarian tumors, 5.6% – cervical cancer, 1.7% of patients have uterine sarcoma. In 68.8% of patients, ovarian cancer is diagnosed in the late stages with a predominance of disease detection at stage III of the tumor process (62.5%). Endometrial cancer is diagnosed in 86.2% of patients in the early stages of the tumor process. The frequency of diagnosis of endometrial cancer in the later stages is 4.6%. From the clinical manifestations of genital tumors in postmenopausal, vaginal bloody discharge and/or bleeding, abdominal pain, rapid heartbeat and excessive body hair growth predominate. Conclusion. The results obtained allowed to establish that the postmenopausal period is a period of high risk for the development of neoplastic processes of the reproductive organs. Key words: genital tumors, postmenopausal period, endometrial cancer, ovarian cancer, uterine myoma, uterine sarcoma, endometrial hyperplasia.