Gravity model in the Korean highway

We investigate the traffic flows of the Korean highway system, which contains both public and private transportation information. We find that the traffic flow T(ij) between city i and j forms a gravity model, the metaphor of physical gravity as described in Newton's law of gravity, P(i)P(j)/r(ij)^2, where P(i) represents the population of city i and r(ij) the distance between cities i and j. It is also shown that the highway network has a heavy tail even though the road network is a rather uniform and homogeneous one. Compared to the highway network, air and public ground transportation establish inhomogeneous systems and have power-law behaviors.Comment: 13 page

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Human Movement Is Both Diffusive and Directed

Understanding the influence of the built environment on human movement requires quantifying spatial structure in a general sense. Because of the difficulty of this task, studies of movement dynamics often ignore spatial heterogeneity and treat movement through journey lengths or distances alone. This study analyses public bicycle data from central London to reveal that, although journey distances, directions, and frequencies of occurrence are spatially variable, their relative spatial patterns remain largely constant, suggesting the influence of a fixed spatial template. A method is presented to describe this underlying space in terms of the relative orientation of movements toward, away from, and around locations of geographical or cultural significance. This produces two fields: one of convergence and one of divergence, which are able to accurately reconstruct the observed spatial variations in movement. These two fields also reveal categorical distinctions between shorter journeys merely serving diffusion away from significant locations, and longer journeys intentionally serving transport between spatially distinct centres of collective importance. Collective patterns of human movement are thus revealed to arise from a combination of both diffusive and directed movement, with aggregate statistics such as mean travel distances primarily determined by relative numbers of these two kinds of journeys

Prevalence of human papillomavirus infection in women in Benin, West Africa

<p>Abstract</p> <p>Background</p> <p>Cervical cancer ranks as the first most frequent cancer among women in Benin. The major cause of cervical cancer now recognized is persistent infection of Human Papillomavirus (HPV). In Benin there is a lack of screening programs for prevention of cervical cancer and little information exists regarding HPV genotype distribution.</p> <p>Methods</p> <p>Cervical cells from 725 women were examined for the presence of viral DNA by means of a polymerase chain reaction (PCR) multiplex-based assay with the amplification of a fragment of L1 region and of E6/E7 region of the HPV genome, and of abnormal cytology by Papanicolaou method. The association between HPV status and Pap test reports was evaluated. Socio-demographic and reproductive characteristics were also related.</p> <p>Results</p> <p>A total of 18 different HPV types were identified, with a prevalence of 33.2% overall, and 52% and 26.7% among women with and without cervical lesions, respectively. Multiple HPV infections were observed in 40.2% of HPV-infected women. In the HPV-testing group, the odds ratio for the detection of abnormal cytology was 2.98 (95% CI, 1.83-4.84) for HPV positive in comparison to HPV negative women. High risk types were involved in 88% of infections, most notably HPV-59, HPV-35, HPV-16, HPV-18, HPV-58 and HPV-45. In multiple infections of women with cytological abnormalities HPV-45 predominated.</p> <p>Conclusions</p> <p>This study provides the first estimates of the prevalence of HPV and type-specific distribution among women from Benin and demonstrates that the epidemiology of HPV infection in Benin is different from that of other world regions. Specific area vaccinations may be needed to prevent cervical cancer and the other HPV-related diseases.</p

Pharmacological Investigations of the Dissociative ‘Legal Highs’ Diphenidine, Methoxphenidine and Analogues

1,2-Diarylethylamines including lanicemine, lefetamine, and remacemide have clinical relevance in a range of therapeutic areas including pain management, epilepsy, neurodegenerative disease and depression. More recently 1,2-diarylethylamines have been sold as ‘legal highs’ in a number of different forms including powders and tablets. These compounds are sold to circumvent governmental legislation regulating psychoactive drugs. Examples include the opioid MT-45 and the dissociative agents diphenidine (DPH) and 2-methoxy-diphenidine (2-MXP). A number of fatal and non-fatal overdoses have been linked to abuse of these compounds. As with many ‘legal highs’, little is known about their pharmacology. To obtain a better understanding, the effects of DPH, 2-MXP and its 3- and 4-MeO- isomers, and 2-Cl-diphenidine (2-Cl-DPH) were investigated using binding studies at 46 central nervous system receptors including the N-methyl-D-aspartate receptor (NMDAR), serotonin, dopamine, norepinephrine, histamine, and sigma receptors as well as the reuptake transporters for serotonin, dopamine and norepinephrine. Reuptake inhibition potencies were measured at serotonin, norepinephrine and dopamine transporters. NMDAR antagonism was established in vitro using NMDAR-induced field excitatory postsynaptic potential (fEPSP) experiments. Finally, DPH and 2-MXP were investigated using tests of pre-pulse inhibition of startle (PPI) in rats to determine whether they reduce sensorimotor gating, an effect observed with known dissociative drugs such as phencyclidine (PCP) and ketamine. The results suggest that these 1,2-diarylethylamines are relatively selective NMDAR antagonists with weak off-target inhibitory effects on dopamine and norepinephrine reuptake. DPH and 2-MXP significantly inhibited PPI. DPH showed greater potency than 2-MXP, acting with a median effective dose (ED50) of 9.5 mg/kg, which is less potent than values reported for other commonly abused dissociative drugs such as PCP and ketamine

Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method.

The use of subgroups based on biological-clinical and socio-demographic variables to deal with population heterogeneity is well-established in public policy. The use of subgroups based on preferences is rare, except when religion based, and controversial. If it were decided to treat subgroup preferences as valid determinants of public policy, a transparent analytical procedure is needed. In this proof of method study we show how public preferences could be incorporated into policy decisions in a way that respects both the multi-criterial nature of those decisions, and the heterogeneity of the population in relation to the importance assigned to relevant criteria. It involves combining Cluster Analysis (CA), to generate the subgroup sets of preferences, with Multi-Criteria Decision Analysis (MCDA), to provide the policy framework into which the clustered preferences are entered. We employ three techniques of CA to demonstrate that not only do different techniques produce different clusters, but that choosing among techniques (as well as developing the MCDA structure) is an important task to be undertaken in implementing the approach outlined in any specific policy context. Data for the illustrative, not substantive, application are from a Randomized Controlled Trial of online decision aids for Australian men aged 40-69 years considering Prostate-specific Antigen testing for prostate cancer. We show that such analyses can provide policy-makers with insights into the criterion-specific needs of different subgroups. Implementing CA and MCDA in combination to assist in the development of policies on important health and community issues such as drug coverage, reimbursement, and screening programs, poses major challenges -conceptual, methodological, ethical-political, and practical - but most are exposed by the techniques, not created by them

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Big data and environmental management: the perspectives of the Regional Environmental Information System of Sardinia, Italy

In the 2000s and in the 2010s, the expression ‘big data’ has come to the fore embracing discourses on technical tools, organizations, and uses of datasets that are too large to be handled through current available technologies (Fosso Wamba et al., 2015). However, this simple definition is problematic. While Batty (2015) argues that some 40 definitions of big data exist, a very popular scheme is provided by the so-called ‘multiple V model’, which implies the description of the volume, velocity, variety, value, and veracity of the dataset at hand (Assunção et al., 2015; Hashem et al., 2015; Russom, 2011). The multiple V model is straightforward and covers, although implicitly, other key aspects such as political stakes, organizational commitment, openness, security, and spatial features of the data involved. The adoption of big data and their analytics in many organizations is challenging, since it is hindered by typical barriers involving technological skills, mentality, data sharing, and privacy issues (Villars, Olofson and Eastwood, 2011; Edosio, 2014). In urban and regional science, a relevant paradigm is that of ‘smart cities’, invoking a process toward more efficient forms of management through continuous use of big and open data analytics disentangling the rationale of complex networks interlaced in urban areas (Batty et al., 2012; Townsend, 2013). Environmental management and planning imply the development of information-intensive processes, not least because the increasing complexity of environmental issues and institutional apparatuses have spurred the need for designing and maintaining large datasets (Vitolo et al., 2015). This contribution develops on the multiple V model defining big data and uses the resulting analytical framework to pinpoint strengths and weaknesses of a possible big data driven evolution of the Regional Environmental Information System (REIS) of Sardinia, Italy