bluuebunny/crossref_metadata_2025 huggingface dataset:
A Target-Detecting Visual Neuron in the Dragonfly Locks on to Selectively Attended Targets
Authors: Lancer Benjamin H. , Evans Bernard J.E. , Fabian Joseph M. , O'Carroll David C. , Wiederman Steven D.
Published: September 2019

The visual world projects a complex and rapidly changing image onto the retina of many animal species. This presents computational challenges for those animals reliant on visual processing to provide an accurate representation of the world. One such challenge is parsing a visual scene for the most salient targets, such as the selection of prey amid a swarm. The ability to selectively prioritize processing of some stimuli over others is known as 'selective attention'. We recently identified a dragonfly visual neuron called 'Centrifugal Small Target Motion Detector 1′ (CSTMD1) that exhibits selective attention when presented with multiple, equally salient targets. Here we conductedin vivo, electrophysiological recordings from CSTMD1 in wild-caught male dragonflies (Hemicordulia tau), while presenting visual stimuli on an LCD monitor. To identify the target selected in any given trial, we uniquely modulated the intensity of the moving targets (frequency tagging). We found that the frequency information of the selected target is preserved in the neuronal response, while the distracter is completely ignored. We also show that the competitive system that underlies selection in this neuron can be biased by the presentation of a preceding target on the same trajectory, even when it is of lower contrast than an abrupt, novel distracter. With this improved method for identifying and biasing target selection in CSTMD1, the dragonfly provides an ideal animal model system to probe the neuronal mechanisms underlying selective attention.

SIGNIFICANCE STATEMENTWe present the first application of frequency tagging to intracellular neuronal recordings, demonstrating that the frequency component of a stimulus is encoded in the spiking response of anindividualneuron. Using this technique as an identifier, we demonstrate that CSTMD1 'locks on' to a selected target and encodes the absolute strength of this target, even in the presence of abruptly appearing, high-contrast distracters. The underlying mechanism also permits the selection mechanism to switch between targets mid-trial, even among equivalent targets. Together, these results demonstrate greater complexity in this selective attention system than would be expected in a winner-takes-all network. These results are in contrast to typical findings in the primate and avian brain, but display intriguing resemblance to observations in human psychophysics.
PiRNA-DQ541777 Contributes to Neuropathic Pain via Targeting Cdk5rap1
Authors: Zhang Chenjing , Sha Huanhuan , Peng Yunan , Wang Yin , Liu Cunming , Zhou Xuelong
Published: September 2019

Piwi-Interacting RNA (piRNA) is the largest class of small noncoding RNA and is involved in various physiological and pathological processes. However, whether it has a role in pain modulation remains unknown. In the present study, we found that spinal piRNA-DQ541777 (piR-DQ541777) was significantly increased in the male mouse model of sciatic nerve chronic constriction injury (CCI)-induced neuropathic pain. Knockdown of spinal piR-DQ541777 alleviated CCI-induced thermal hyperalgesia and mechanical allodynia and spinal neuronal sensitization. However, the overexpression of spinal piR-DQ541777 in naive mice produced pain behaviors and increased spinal neuron sensitization. Furthermore, we found that piR-DQ541777 regulates pain behaviors by targeting CDK5 regulatory subunit-associated protein 1 (Cdk5rap1). CCI increased the methylation level of CpG islands in thecdk5rap1promoter and consequently reduced the expression of Cdk5rap1, which was reversed by the knockdown of piR-DQ541777 and mimicked by the overexpression of piR-DQ541777 in naive mice. Finally, piR-DQ541777 increased the methylation level of CpG islands by recruiting DNA methyltransferase 3A (DNMT3a) tocdk5rap1promoter. In conclusion, this study represents a novel role of piR-DQ541777 in the regulation of neuropathic pain through the methylation ofcdk5rap1.

SIGNIFICANCE STATEMENTChronic pain affects ∼20% of the population of the world and is a major global public health problem. Although we have studied the neurobiological mechanism of neuropathic pain for decades, there is still no ideal drug available to treat it. This work indicates that a novel role of Piwi-interacting RNA (piRNA) DQ541777 in the regulation of neuropathic pain through the methylation ofcdk5rap1. Our findings provide the first evidence of the regulatory effect of piRNAs on neuropathic pain, which may improve our understanding of pain mechanisms and lead to the discovery of novel drug targets for the prevention and treatment of neuropathic pain.
Association of serum lipids with β-cell function in obese children and adolescents
Authors: Bedogni Giorgio , Mari Andrea , De Col Alessandra , Tamini Sofia , Gastaldelli Amalia , Sartorio Alessandro
Published: September 2019

Few data are available on the association between serum lipids and insulin secretion (ISEC) in children. We evaluated the association of triglycerides (TG), HDL cholesterol (HDL-C) and LDL cholesterol (LDL-C) with ISEC in 1150 non-diabetic obese children and adolescents using multivariable robust median regression. The following models were employed: (1) IGI or incAUCR as the ISEC response variable; (2) QUICKI, OGIS, the Stumvoll index or the Matsuda insulin sensitivity index as the insulin sensitivity (ISEN) predictor; (3) TG, HDL-C and LDL-C as the predictors of interest; (4) 120-min glucose, age, sex and body mass index as confounders. LDL-C and TG were not associated with ISEC in any model. In three out of four IGI models, an increase of 1 interquartile range (IQR) of HDL-C was associated with a decrease of median incAUCR ranging from −9 (robust 95% CI −17 to −2) to −8 (−14 to −1) pmol/mmol. In two out of four incAUCR models, an increase of 1 IQR of HDL-C was associated with a decrease of median IGI ranging from −8 (−15 to −1) to −7 (−11 to −2) pmol/mmol. TG and LDL-C are not associated and HDL-C is inversely associated with ISEC in obese children and adolescents.
Mice lacking adenosine 2A receptor reveal increased severity of MCD-induced NASH
Authors: Zhou Jing , Li Honggui , Cai Yuli , Ma Linqiang , Matthews Destiny , Lu Bangchao , Zhu Bilian , Chen Yanming , Qian Xiaoxian , Xiao Xiaoqiu , Li Qifu , Guo Shaodong , Huo Yuqing , Zhao Liang , Tian Yanan , Li Qingsheng , Wu Chaodong
Published: September 2019

Adenosine 2A receptor (A2AR) exerts a protective role in obesity-related non-alcoholic fatty liver disease. Here, we examined whether A2AR protects against non-alcoholic steatohepatitis (NASH). In C57BL/6J mice, feeding a methionine- and choline-deficient diet (MCD) resulted in significant weight loss, overt hepatic steatosis, and massive aggregation of macrophages in the liver compared with mice fed a chow diet. MCD feeding also significantly increased the numbers of A2AR-positive macrophages/Kupffer cells in liver sections although decreasing A2AR amount in liver lysates compared with chow diet feeding. Next, MCD-induced NASH phenotype was examined in A2AR-disrupted mice and control mice. Upon MCD feeding, A2AR-disruptd mice and control mice displayed comparable decreases in body weight and fat mass. However, MCD-fed A2AR-disrupted mice revealed greater liver weight and increased severity of hepatic steatosis compared with MCD-fed control mice. Moreover, A2AR-disupted mice displayed increased severity of MCD-induced liver inflammation, indicated by massive aggregation of macrophages and increased phosphorylation states of Jun-N terminal kinase (JNK) p46 and nuclear factor kappa B (NFκB) p65 and mRNA levels of tumor necrosis factor alpha, interleukin-1 beta, and interleukin-6. In vitro, incubation with MCD-mimicking media increased lipopolysaccharide (LPS)-induced phosphorylation states of JNK p46 and/or NFκB p65 and cytokine mRNAs in control macrophages and RAW264.7 cells, but not primary hepatocytes. Additionally, MCD-mimicking media significantly increased lipopolysaccharide-induced phosphorylation states of p38 and NFκB p65 in A2AR-deficient macrophages, but insignificantly decreased lipopolysaccharide-induced phosphorylation states of JNK p46 and NFκB p65 in A2AR-deficient hepatocytes. Collectively, these results suggest that A2AR disruption exacerbates MCD-induced NASH, which is attributable to, in large part, increased inflammatory responses in macrophages.
The role of the adiponectin system in acute fasting-impaired mouse ovaries
Authors: Han Yingying , Zhang Shuhao , Zhuang Haotong , Fan Sijie , Yang Jiayi , Zhao Liwei , Bao Weidong , Gao Fuli , Zhang Haolin , Yuan Zhengrong , Weng Qiang
Published: September 2019

Adiponectin (ADIPOQ, encoded by Adipoq) is an important white adipose-derived adipokine linked to energy homeostasis and reproductive function. This study aims to reveal the expression and role of the adiponectin system in the ovaries under acute malnutrition. In this study, 48-h food deprivation significantly inhibited ovarian growth by suppressing cell proliferation and inducing cell apoptosis in the ovaries of gonadotrophin-primed immature mice. It was also accompanied by significantly decelerated basic metabolism (glucose, triacylglycerol and cholesterol), varied steroid hormones (follicle-stimulating hormone, luteinizing hormone and estradiol) and vanishment of the peri-ovarian fat. It is noteworthy that after acute fasting, the adiponectin levels in ovaries rather than in blood were significantly elevated. Immunohistochemical study demonstrated that adiponectin and its receptors (ADIPOR1 and ADIPOR2) primarily appeared in ovarian somatic and/or germ cells, and their protein expressions were upregulated in the ovaries from fasted mice. Further in vitro study verified that ADIPOR1/2 agonist obviously inhibited follicle-stimulating hormone-induced oocyte meiotic resumption, while the antagonist significantly enhanced the percentage of oocyte maturation in the absence of follicle-stimulating hormone. Furthermore, the build up of peri-ovarian fat under physiological status in mice showed a positive correlation with both the hypertrophy of adipocytes and growth of ovaries. Taken together, these findings indicate that the upregulation of the adiponectin system disturbs the normal female reproductive function under the malnutrition status, and it may be associated with the loss of peri-ovarian fat depots.
SERPINA1 and HSD17B13 Gene Variants in Patients with Liver Fibrosis and Cirrhosis
Authors: Basyte-Bacevice Viktorija , Skieceviciene Jurgita , Valantiene Irena , Sumskiene Jolanta , Petrenkiene Vitalija , Kondrackiene Jurate , Petrauskas Dalius , Lammert Frank , Kupcinskas Juozas
Published: September 2019

Background & Aims: Two single nucleotide polymorphisms (SNPs) in SERPINA1 (Pi*Z rs28929474 and Pi*Srs17580) are risk factors for developing liver cirrhosis. A recent study identified a common SNP in HSD17B13(rs72613567) that conferred protection from chronic liver disease. The aim of the present study was to testthese associations in a cohort of Lithuanian patients with liver fibrosis or cirrhosis.

Methods: The study included 302 patients with cirrhosis, 127 patients with liver fibrosis (METAVIR stagesI-III) and 548 controls, all from Lithuania. SNPs were genotyped by quantitative PCR, using TaqMan allelicdiscrimination assays. Adjusted p value of ≤ 0.016 was considered significant.

Results: Genotype distributions of SERPINA1 and HSD17B13 SNPs were in Hardy-Weinberg equilibrium.SERPINA1 Pi*Z was not associated with liver fibrosis or cirrhosis. HSD17B13 rs10433937 (in high linkagedisequilibrium with rs72613567; r 2 =0.96) also showed no overall association with liver disease, but the GG-genotype was associated with reduced risk of liver fibrosis (aOR 0.37, p=0.03). SERPINA1 Pi*S was associatedwith higher risk of developing hepatic fibrosis (aOR 3.42, p=0.001) and cirrhosis (aOR 2.59, p=0.02).

Conclusions: We found that SERPINA1 Pi*S variant conferred an increased risk of developing liver fibrosis,while SERPINA1 Pi*Z and HSD17B13 rs10433937 were not associated with liver fibrosis or cirrhosis ofdifferent aetiology.
Routine Liver Elastography Could Predict Actuarial Survival after Liver Transplantation
Authors: Pfeiffenberger Jan , Hornuss Daniel , Houben Philip , Wehling Cyrill , Von Haken Rebecca , Loszanowski Vladimir , Mieth Markus , Mehrabi Arianeb , Weiss Karl-Heinz , Friedrich Kilian
Published: September 2019

Background & Aims: Transient elastography (TE) has routinely been implemented in the diagnosis and assessment of chronic liver disease. Little data are available in the post liver transplant (LTx) setting.

Methods: Three months after LTx, we performed TE in 137 liver transplant recipients and investigated its predictive value upon further clinical outcome. The mean follow-up time for clinical outcome was 24 months.

Results: Mean TE value was 10.6 kPa (± 6.3 kPa; range 2.8 – 29.9 kPa). There was a significant correlation between TE and aspartate aminotransferase (AST) (p=0.004), gamma-glutamyl transferase (GGT) (p=0.031) and bilirubin (p<0.001) serum levels. In Cox univariate analysis, TE served as a predictor of actuarial survival free of liver transplantation (OR=1.111, 95%CI: 1.051–1.174; p<0.001). In multivariate analysis, TE remained an independent risk factor associated with reduced actuarial survival free of liver transplantation (OR=1.080, 95%CI: 1.001–1.166; p=0.047), along with thrombocytes (OR=0.992, 95%CI: 0.986–0.999; p=0.020) and metabolic co-disease (OR = 0.250, 95%CI: 0.070–0.895; p=0.033).

Conclusion: Transient elastography measurement at three months after LTx seems a robust predictor of survival in liver transplant recipients.
Functional Emesis
Authors: Popa Stefan Lucian , Chiarioni Giuseppe , David Liliana , Golea George Ionut , Dumitrascu Dan Lucian
Published: September 2019

Background: Emesis is an essential survival response, because consumption of food is a key element in the survival behavior and the act of feeding can expose the majority of internal organs to toxins, altered food, viruses, bacteria or fungi. The relationship between visceral or vestibular stimuli and the anatomical structures of the brain-gut axis explain the modulation of the gastrointestinal response. We aimed to review comprehensively the current knowledge on emesis occurring in the disorders of brain-gut interaction (DBGI), i.e. functional conditions presenting with emesis.

Methods: PubMed, Cochrane Library, EMBASE and WILEY databases were screened for relevant publications from the last 20 years regarding brain-gut disorders manifesting emesis. We included in the search also idiopathic gastroparesis, as a condition paralleling the postprandial distress syndrome.

Results: We critically analyzed 2 articles about chronic nausea and vomiting syndrome, 9 articles about cyclic vomiting syndrome, 9 articles about cannabinoid hyperemesis, 10 articles about idiopathic gastroparesis and 6 articles about gastroparesis like-syndrome. Contradictory results regarding the implication of environmental factors in the development of emesis in DBGI demonstrate that the underlying pathogenesis is still not completely understood.
Color Coded Perfusion Imaging with Contrast Enhanced Ultrasound (CEUS) for Post-Interventional Success Control Following Irreversible Electroporation (IRE) of Primary and Secondary Malignant Liver Lesions
Authors: Rennert Janine , Wiesinger Isabel , Schicho Andreas , Wiggermann Philipp , Stroszczynski Christian , Beyer Lukas Philipp , Jung Ernst Michael
Published: September 2019

Aim: Evaluation of the post-interventional success following irreversible electroporation (IRE) using a new color coded perfusion quantification software with contrast-enhanced ultrasound (CEUS) in patients with malignant lesions of the liver.

Methods: Thirty-eight patients with 68 malignant liver lesions underwent IRE. All malignant lesions were investigated with CEUS before and within 24 hours following IRE to detect residual tumor tissue. The parameters analyzed by color coded perfusion quantification software were: the peak enhancement (pE), time to peak (TTP), mean transit time (mTT), rise (Ri) and wash-in area under the curve (WiAUC). Perfusion in the center, the margins of the lesions and in the surrounding liver were evaluated using these parameters.

Results: Hepatocellular carcinoma (HCC) with complete ablation showed significantly different changes between the center and the margin of the lesions for WiAUC (p<0.05) and pE (p<0.01). Also significant differences were noted between the center of the lesions and the surrounded tissue for the same parameters (p<0.01). In the completely ablated metastatic lesions, significant differences were found between the center of the lesion and the margins (p < 0.01) and between the center of the lesion and the surrounding liver (p < 0.05) for WiAUC. mTT, TTP and Ri showed no significant changes between the center of the lesions, margin of the lesions or surrounding tissue. Also, no significant differences were found for these parameters in the different regions of interest for HCC or the metastatic lesions with partial ablation success.

Conclusion: CEUS with perfusion imaging is a valuable supporting tool for the post-interventional evaluation of liver lesions following IRE. Focus should be placed on the peak enhancement (pE) and the wash-in area under the curve (WiAUC).
Percutaneous Endoscopic Gastrostomy with Jejunal Extension Tube for the Delivery of Levodopa Carbidopa Intestinal Gel: Clinical Practice Guidelines of the Romanian Society of Digestive Endoscopy
Authors: Gheorghe Cristian , Dumitru Eugen , Ciocîrlan Mihai , Dobru Daniela , Drug Vasile , Goldis Adrian , Jinga Mariana , Manuc Mircea , Saftoiu Adrian , Seicean Andrada , Cotruta Bogdan , Bancila Ion , Pitigoi Dan , Mercea Voicu Alexandru , Tanțău Marcel
Published: September 2019

Percutaneous endoscopic gastrostomy with jejunal extension (PEG/J) was first described in 1998 and has become the standard technique for fixing the tube in place for levodopa carbidopa intestinal gel (LCIG) infusion. The Romanian Society of Digestive Endoscopy (RSDE) decided to create a consensus paper to meet the needs in medical training and practice. After reviewing the available published data and existing recommendations, a consensus process was carried out involving the leaders of opinion in this field. The resulting text and recommendations were approved, after reaching expert consensus, and reflects the views of the RSDE for the best practice of PEG/J tube placement. The pull through method (“pull technique”) is the prevailing PEG-tube placement procedure in Romania. The procedure can be performed with intravenous sedation combined with local anesthesia. Although minor complications are common, serious complications are infrequent, and the tube insertion procedures have a good safety record. Redo procedures are sometimes necessary and clinicians should be aware of these situations.
A Rare Case of Ciliated Foregut Cyst of the Common Hepatic Duct
Authors: Zaharie Roxana , Mois Emil , Al Hajjar Nadim , Zdrehuș Claudiu , Rusu Ioana , Zaharie Florin
Published: September 2019

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Racial Differences in Sepsis Recognition in the Emergency Department
Authors: Raman Jenny , Johnson Tiffani J. , Hayes Katie , Balamuth Fran
Published: September 2019

OBJECTIVES:

We assessed racial differences in sepsis recognition in a pediatric emergency department (ED) with an established electronic sepsis alert system.

METHODS:

Quality-improvement data from June 1, 2016 to May 31, 2017 was used in this retrospective cohort study. All ED visits were included for non-Hispanic black (NHB) and non-Hispanic white (NHW) patients. The sepsis pathway was activated through the alert, 2 stages and a huddle, or outside of the alert using clinician judgment alone. We evaluated racial differences in the frequency of alerts and sepsis pathway activation within and outside of the alert. Multivariable regression adjusted for high-risk condition, sex, age, and insurance.

RESULTS:

There were 97 338 ED visits: 56 863 (58.4%) and 23 008 (23.6%) from NHBs and NHWs, respectively. NHWs were more likely than NHBs to have a positive second alert (adjusted odds ratio [aOR] 2.4; 95% confidence interval [CI] 2.1–2.8). NHWs were more likely than NHBs to have the sepsis pathway activated (aOR 1.4; 95% CI 1.02–2.1). Of those treated within the alert, there was no difference in pathway activation (aOR 0.93; 95% CI 0.62–1.4). Of those recognized by clinicians when the alert did not fire, NHWs were more likely than NHBs to be treated (aOR 3.4; 95% CI 1.8–6.4).

CONCLUSIONS:

NHWs were more likely than NHBs to be treated for sepsis, although this difference was specifically identified in the subset of patients treated for sepsis outside of the alert. This suggests that an electronic alert reduces racial differences compared with clinician judgment alone.
Introduction: Evidence for entheogen use in prehistory and world religions
Authors: Winkelman Michael
Published: September 2019

This introduction to the special issue reviews research that supports the hypothesis that psychedelics, particularly psilocybin, were central features in the development of religion. The greater response of the human serotonergic system to psychedelics than is the case for chimpanzees’ serotonergic receptors indicates that these substances were environmental factors that affected hominin evolution. These substances also contributed to the evolution of ritual capacities, shamanism, and the associated alterations of consciousness. The role of psilocybin mushrooms in the ancient evolution of human religions is attested to fungiform petroglyphs, rock artifacts, and mythologies from all major regions of the world. This prehistoric mycolatry persisted into the historic era in the major religious traditions of the world, which often left evidence of these practices in sculpture, art, and scriptures. This continuation of entheogenic practices in the historical world is addressed in the articles here. But even through new entheogenic combinations were introduced, complex societies generally removed entheogens from widespread consumption, restricted them in private and exclusive spiritual practices of the leaders, and often carried out repressive punishment of those who engaged in entheogenic practices.
Housing Identification of Citizens as a Reflection of Housing Inequality (Based on Three Cities of Kirov Region)
Authors: Starikova Maria
Published: September 2019

The subject of discussion in the article is housing inequality in a region on the example of cities in Kirov region. The author analyzes the content components of housing inequality, environmental and personal resources that promote and impede the preservation and improvement of housing status. The empirical base of the study was the results of a semi-formalized survey of citizens and expert interviews with realtors conducted in summer 2018 in three cities of Kirov region: Kirov, Vyatskiye Polyany and Kirovo-Chepetsk. The housing "portraits" of citizens are presented, the specificity of the housing plans, practices and self-identification of residents of regional and district cities is analyzed. The empirical study leads to the conclusion that the housing mentality of citizens is characterized by "healthy optimism" and relatively low demands, since the majority of respondents, even against the background of modest opportunities and a "bunch" of housing problems, highly appreciate the quality of their housing and are generally satisfied with its condition. Housing mobility of citizens is mainly reduced to movements within the same housing class. Radical improvements at the expense of available resources can afford only a few, although the population is increasingly using borrowed funds for the arrangement.
Religious Practices Online: Belarus Experience
Authors: Beznjuk Dmitry , Tsybulskaya Natalya
Published: September 2019

The authors deal with the problems of transformation of religion in the information society, in particular, the modification of religious practices updated on the Internet. Digitalization appears in modern society and culture not only as a technical and technological phenomenon, but also as a socio-cultural challenge, which is to be answered by the religious sphere. Under the influence of the global process of digitalization religion, as one of the forms of public consciousness, finds and implements innovative ways of practice. In the structure of these practices, as the authors show in the article, the information and communication element is the most suitable for digitalization. The article reveals the content of the concept of "religious practices" as a continuation of the evolutionary chain of development and transformation of meanings originating in social philosophy: social action – social communication – social practices. The researchers consider the essence of the main provisions of the theory of practices by Theodore Shatsky. The paper reveals the essence of religious practices, describes their features, presents typologies of religious practices on various grounds. The division of social practices into conventional and innovative (according to V. Radaev) as a variant of assessing the prospects and orientation of digitalization in the religious sphere is approved. The phenomenon of digitalization of religious practices is associated with new opportunities to meet the religious needs of the believer. The authors give some examples of digitization of religious practices in foreign religious communities, which demonstrate the capabilities and technological features of this process, reveal its content and likely social consequences. The paper describes the main characteristics of the religious field of modern Belarus, in order to understand the conditions of digitization of religious practices and the presentation of the national and historical specifics of Belarus. The researchers give a brief overview of the representation of religious organizations in Minsk on the Internet (due to the greatest representativeness of religious diversity). They suggest the results of the analysis of the sites of religious communities in Minsk, which make it possible to assess the degree and results of digitization of the religious sphere of Belarus, the readiness of religious organizations to operate in the digital space, the scale of consumption of digital religious products.
Social Prevention of Sociopathic Phenomena in the System of Antideviant Policy
Authors: Baranovsky Nikolai
Published: September 2019

In modern conditions of the systemic transformation of the Belarusian society, which is developing very slowly and contradictory, despite the steady trend of reducing crime, drunkenness, drug addiction and other negative social deviations, the problem of preventing and combating antisocial phenomena continues to be complex and cause serious public concern. In this regard, the relevance and social significance of empirical studies of antisocial phenomena, their dynamics, the specifics of distribution and determination, the study of the current national legislation and social practice of the state criminological and other antideviant policies are growing. Problems of prevention and counteraction to anti-social phenomena have long attracted the attention of criminologists, sociologists, culturologists, teachers, psychologists and other researchers. However, each historical period of time brings many new circumstances that have an impact on the sociopathic situation. In addition, the persistently high level of antisocial behavior in society in different socio-demographic groups of the population indicates the presence of obvious gaps in the scientific knowledge of this problem, especially in the field of genesis and determination of negative social deviations as well as the lack of effectiveness of the forms and methods of preventive work of social actors, such as law enforcement and other public administration bodies, the family as a social institution, public associations, etc. The author carries out theoretical and applied design of an innovative system of social prevention of antisocial phenomena based on the analysis of statistical data and sociological research. It includes a set of general, special and individual measures of social, victimological and sociopathic, socio-reintegration and legal preventive activities that should be systematically implemented at the social, situational, personality-deviant, personality-victim and personality-socio-interactive levels of social subjectivity. The researcher pays special attention to the state, dynamics and peculiarities of the etiology of the alcohol and drug situation in the country. The authors propose a set of specific social and practical measures to improve the prevention of alcoholism and drug addiction which have a negative impact on the sustainable development of modern Belarusian society.
Prerequisites for the development of dermatovenerological care, taking into account the implementation of the national project «Health»
Authors: Kubanova Anna A. , Martynov Andrey A. , Vlasova Anna V.
Published: September 2019

The article discusses the prerequisites for the development of dermatovenerological assistance in the Russian Federation, taking into account the implementation of the national project Health. The analysis of the state of the dermatovenerological service in the Russian Federation. Prior to the implementation of the Healthcare project, the authors attempted to justify promising directions for the development of dermatovenereological assistance in the country. Venereology clinics need to be reoriented, in particular, to provide specialized medical care to foreign citizens, as well as the introduction of new types of medical care, in particular, medical rehabilitation, as well as the development of the service component of the main activity. It is assumed that the implementation of priorities in the framework of the main direction of the strategic development of the Russian Federation Healthcare for the period up to 2025 will be aimed at solving issues important for health. Systematic work in this direction should ensure an increase in the availability and quality of medical care to the population of the Russian Federation, including in the dermatovenereology profile.
Unfancifying wellness
Authors: Dow Margaret
Published: September 2019

This article was migrated. The article was not marked as recommended. Our well-intended focus on wellness may be contributing to fancy, unattainable goals. Formal programming appears to be helpful in improving resident well-being and burnout, but we may be neglecting the power of modelling the behaviors that have helped us survive as practicing physicians. Emphasizing daily self-care practices may contribute to a realistic, sustainable path to improved health and wellbeing to complement our more structured efforts.
Perceptual-Sensitive GAN for Generating Adversarial Patches
Authors: Liu Aishan , Liu Xianglong , Fan Jiaxin , Ma Yuqing , Zhang Anlan , Xie Huiyuan , Tao Dacheng
Published: September 2019

Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world. However, existing attack strategies are still far from generating visually natural patches with strong attacking ability, since they often ignore the perceptual sensitivity of the attacked network to the adversarial patch, including both the correlations with the image context and the visual attention. To address this problem, this paper proposes a perceptual-sensitive generative adversarial network (PS-GAN) that can simultaneously enhance the visual fidelity and the attacking ability for the adversarial patch. To improve the visual fidelity, we treat the patch generation as a patch-to-patch translation via an adversarial process, feeding any types of seed patch and outputting the similar adversarial patch with high perceptual correlation with the attacked image. To further enhance the attacking ability, an attention mechanism coupled with adversarial generation is introduced to predict the critical attacking areas for placing the patches, which can help producing more realistic and aggressive patches. Extensive experiments under semi-whitebox and black-box settings on two large-scale datasets GTSRB and ImageNet demonstrate that the proposed PS-GAN outperforms state-of-the-art adversarial patch attack methods.
Joint Representation Learning for Multi-Modal Transportation Recommendation
Authors: Liu Hao , Li Ting , Hu Renjun , Fu Yanjie , Gu Jingjing , Xiong Hui
Published: September 2019

Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation systems can help to satisfy the diversified needs of travelers and improve the efficiency of transport networks. However, existing transport recommender systems mainly focus on unimodal transport planning. To this end, in this paper, we propose a joint representation learning framework for multi-modal transportation recommendation based on a carefully-constructed multi-modal transportation graph. Specifically, we first extract a multi-modal transportation graph from large-scale map query data to describe the concurrency of users, Origin-Destination (OD) pairs, and transport modes. Then, we provide effective solutions for the optimization problem and develop an anchor embedding for transport modes to initialize the embeddings of transport modes. Moreover, we infer user relevance and OD pair relevance, and incorporate them to regularize the representation learning. Finally, we exploit the learned representations for online multimodal transportation recommendations. Indeed, our method has been deployed into one of the largest navigation Apps to serve hundreds of millions of users, and extensive experimental results with real-world map query data demonstrate the enhanced performance of the proposed method for multimodal transportation recommendations.
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Authors: Shang Junyuan , Xiao Cao , Ma Tengfei , Li Hongyan , Sun Jimeng
Published: September 2019

Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.
Improving Search with Supervised Learning in Trick-Based Card Games
Authors: Solinas Christopher , Rebstock Douglas , Buro Michael
Published: September 2019

In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values. While the evaluation component is vital, the accuracy of move value estimates is also fundamentally linked to how well the sampling distribution corresponds the true distribution. Despite this, recent work in trick-taking card game AI has mainly focused on improving evaluation algorithms with limited work on improving sampling. In this paper, we focus on the effect of sampling on the strength of a player and propose a novel method of sampling more realistic states given move history. In particular, we use predictions about locations of individual cards made by a deep neural network — trained on data from human gameplay — in order to sample likely worlds for evaluation. This technique, used in conjunction with Perfect Information Monte Carlo (PIMC) search, provides a substantial increase in cardplay strength in the popular trick-taking card game of Skat.
Outlier Aware Network Embedding for Attributed Networks
Authors: Bandyopadhyay Sambaran , Lokesh N. , Murty M. N.
Published: September 2019

Attributed network embedding has received much interest from the research community as most of the networks come with some content in each node, which is also known as node attributes. Existing attributed network approaches work well when the network is consistent in structure and attributes, and nodes behave as expected. But real world networks often have anomalous nodes. Typically these outliers, being relatively unexplainable, affect the embeddings of other nodes in the network. Thus all the downstream network mining tasks fail miserably in the presence of such outliers. Hence an integrated approach to detect anomalies and reduce their overall effect on the network embedding is required.Towards this end, we propose an unsupervised outlier aware network embedding algorithm (ONE) for attributed networks, which minimizes the effect of the outlier nodes, and hence generates robust network embeddings. We align and jointly optimize the loss functions coming from structure and attributes of the network. To the best of our knowledge, this is the first generic network embedding approach which incorporates the effect of outliers for an attributed network without any supervision. We experimented on publicly available real networks and manually planted different types of outliers to check the performance of the proposed algorithm. Results demonstrate the superiority of our approach to detect the network outliers compared to the state-of-the-art approaches. We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique. The source code is made available at https://github.com/sambaranban/ONE.
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Authors: Wu Bingzhe , Zhang Xiaolu , Zhao Shiwan , Xie Lingxi , Zeng Caihong , Liu Zhihong , Sun Guangyu
Published: September 2019

Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major difficulties in collecting data and training a vision-based algorithm to assist nephropathy diagnosis.This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification. Our approach, named generator-to-classifier (G2C), is a twostage framework. Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has). We optimize these two stages in a joint manner. To provide a reasonable initialization, we pre-train the generators in an unlabeled reference set under an unpaired image-to-image translation task, and then fine-tune them together with the classifier.We conduct experiments on a glomerulus type classification dataset collected by ourselves (there are no publicly available datasets for this purpose). Although joint optimization slightly harms the authenticity of the generated patches, it boosts classification performance, suggesting more effective visual cues are extracted in an automatic way. We also transfer our model to a public dataset for breast cancer classification, and outperform the state-of-the-arts significantly.
One-Class Adversarial Nets for Fraud Detection
Authors: Zheng Panpan , Yuan Shuhan , Wu Xintao , Li Jun , Lu Aidong
Published: September 2019

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator of a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art oneclass classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.
Solving Integer Quadratic Programming via Explicit and Structural Restrictions
Authors: Eiben Eduard , Ganian Robert , Knop Dusan , Ordyniak Sebastian
Published: September 2019

We study the parameterized complexity of Integer Quadratic Programming under two kinds of restrictions: explicit restrictions on the domain or coefficients, and structural restrictions on variable interactions. We argue that both kinds of restrictions are necessary to achieve tractability for Integer Quadratic Programming, and obtain four new algorithms for the problem that are tuned to possible explicit restrictions of instances that we may wish to solve. The presented algorithms are exact, deterministic, and complemented by appropriate lower bounds.
Stochastic Submodular Maximization with Performance-Dependent Item Costs
Authors: Fukunaga Takuro , Konishi Takuya , Fujita Sumio , Kawarabayashi Ken-ichi
Published: September 2019

We formulate a new stochastic submodular maximization problem by introducing the performance-dependent costs of items. In this problem, we consider selecting items for the case where the performance of each item (i.e., how much an item contributes to the objective function) is decided randomly, and the cost of an item depends on its performance. The goal of the problem is to maximize the objective function subject to a budget constraint on the costs of the selected items. We present an adaptive algorithm for this problem with a theoretical guaran-√ tee that its expected objective value is at least (1−1/ 4 e)/2 times the maximum value attained by any adaptive algorithms. We verify the performance of the algorithm through numerical experiments.
Separator-Based Pruned Dynamic Programming for Steiner Tree
Authors: Iwata Yoichi , Shigemura Takuto
Published: September 2019

Steiner tree is a classical NP-hard problem that has been extensively studied both theoretically and empirically. In theory, the fastest approach for inputs with a small number of terminals uses the dynamic programming, but in practice, stateof-the-art solvers are based on the branch-and-cut method. In this paper, we present a novel separator-based pruning technique for speeding up a theoretically fast DP algorithm. Our empirical evaluation shows that our pruned DP algorithm is quite effective against real-world instances admitting small separators, scales to more than a hundred terminals, and is competitive with a branch-and-cut solver.
On Sampling Complexity of the Semidefinite Affine Rank Feasibility Problem
Authors: Molybog Igor , Lavaei Javad
Published: September 2019

In this paper, we study the semidefinite affine rank feasibility problem, which consists in finding a positive semidefinite matrix of a given rank from its linear measurements. We consider the semidefinite programming relaxations of the problem with different objective functions and study their properties. In particular, we propose an analytical bound on the number of relaxations that are sufficient to solve in order to obtain a solution of a generic instance of the semidefinite affine rank feasibility problem or prove that there is no solution. This is followed by a heuristic algorithm based on semidefinite relaxation and an experimental proof of its performance on a large sample of synthetic data.
Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets
Authors: Rector-Brooks Jarrid , Wang Jun-Kun , Mozafari Barzan
Published: September 2019

We revisit the Frank-Wolfe (FW) optimization under strongly convex constraint sets. We provide a faster convergence rate for FW without line search, showing that a previously overlooked variant of FW is indeed faster than the standard variant. With line search, we show that FW can converge to the global optimum, even for smooth functions that are not convex, but are quasi-convex and locally-Lipschitz. We also show that, for the general case of (smooth) non-convex functions, FW with line search converges with high probability to a stationary point at a rate of O(1/t), as long as the constraint set is strongly convex—one of the fastest convergence rates in non-convex optimization.
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Authors: Wilder Bryan , Dilkina Bistra , Tambe Milind
Published: September 2019

Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision. However, the loss function used to train the model may easily be misaligned with the end goal, which is to make the best decisions possible. Hand-tuning the loss function to align with optimization is a difficult and error-prone process (which is often skipped entirely).We focus on combinatorial optimization problems and introduce a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions. Technically, our contribution is a means of integrating common classes of discrete optimization problems into deep learning or other predictive models, which are typically trained via gradient descent. The main idea is to use a continuous relaxation of the discrete problem to propagate gradients through the optimization procedure. We instantiate this framework for two broad classes of combinatorial problems: linear programs and submodular maximization. Experimental results across a variety of domains show that decisionfocused learning often leads to improved optimization performance compared to traditional methods. We find that standard measures of accuracy are not a reliable proxy for a predictive model’s utility in optimization, and our method’s ability to specify the true goal as the model’s training objective yields substantial dividends across a range of decision problems.