Quality of life perspectives in long-term care: staff and patient perceptions

In the rehabilitation of chronically mentally ill patients, social impairments and multiple disabilities necessitate the use of comprehensive and complex services encompassing many areas of life. It is suggested that quality of life (QoL) can be used as an organizing framework for long-term care. Subjects in the present study included staff and patients from a rehabilitation unit that offers individualized long-term care, day services, inpatient and outreach facilities. Using subjective indices, staff and patient perceptions of QoL were compared across several life domains. Significant differences between the 2 groups were found in several areas, including leisure activities, law and safety, and health. The implications of these findings for long-term care are discussed.

Observation of tt[over ¯]H Production

The observation of Higgs boson production in association with a top quark-antiquark pair is reported, based on a combined analysis of proton-proton collision data at center-of-mass energies of sqrt[s]=7, 8, and 13 TeV, corresponding to integrated luminosities of up to 5.1, 19.7, and 35.9  fb^{-1}, respectively. The data were collected with the CMS detector at the CERN LHC. The results of statistically independent searches for Higgs bosons produced in conjunction with a top quark-antiquark pair and decaying to pairs of W bosons, Z bosons, photons, τ leptons, or bottom quark jets are combined to maximize sensitivity. An excess of events is observed, with a significance of 5.2 standard deviations, over the expectation from the background-only hypothesis. The corresponding expected significance from the standard model for a Higgs boson mass of 125.09 GeV is 4.2 standard deviations. The combined best fit signal strength normalized to the standard model prediction is 1.26_{-0.26}^{+0.31}.

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals' physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances.

Discovery of the Showdomycin Gene Cluster from Streptomyces showdoensis ATCC 15227 Yields Insight into the Biosynthetic Logic of C-Nucleoside Antibiotics

Nucleoside antibiotics are a large class of pharmaceutically relevant chemical entities, which exhibit a broad spectrum of biological activities. Most nucleosides belong to the canonical N-nucleoside family, where the heterocyclic unit is connected to the carbohydrate through a carbon-nitrogen bond. However, atypical C-nucleosides were isolated from Streptomyces bacteria over 50 years ago, but the molecular basis for formation of these metabolites has been unknown. Here, we have sequenced the genome of S. showdoensis ATCC 15227 and identified the gene cluster responsible for showdomycin production. Key to the detection was the presence of sdmA, encoding an enzyme of the pseudouridine monophosphate glycosidase family, which could catalyze formation of the C-glycosidic bond. Sequence analysis revealed an unusual combination of biosynthetic genes, while inactivation and subsequent complementation of sdmA confirmed the involvement of the locus in showdomycin formation. The study provides the first steps toward generation of novel C-nucleosides by pathway engineering.

Health problems among the street children of Dharan municipality

Background: The street children, a marginalised and vulnerable population to poor health, have grown all over the world and also in our country. The continuous exposure to harsh environment and nature of their life style threatens their mental, physical, social and spiritual well being. With the increasing number the problem is also growing at an alarming proportion. It is therefore important to have baseline data on their health problems. Objectives: This study was conducted to identify the physical health problems among the street children of Dharan Municipality, Nepal. Materials and methods: This is a cross sectional descriptive study. Forty eight subjects were included in the study. Research instruments included an interview schedule, physical health examination performa and lab investigations (i.e. blood for haemoglobin, urine routine examination/microscopic examination, stool routine examination/ microscopic examination). Results: Study results showed that 68.8% of the street children were between 11-15 years of age, 95.8% were males. Out of the total subjects 81.2% were found to be rag pickers. Research findings reveal that 100% of the subjects had at least one or more health problems. The study revealed that majority 87.5% had the habit of cigarette smoking, 50% had habit of consuming alcohol and 72.9% had the habit of taking drug. Dendrite (glue sniffing) was the only drug used by the respondents in this study. The most common health problems were head lice infestation (81.2%), headache (66.7%), cut injury (60.4%), common cold (52.1%), dental caries (52%), burning micturation (47.9%), cough (47.9%), underweight (43.8%), abdominal pain (39.6%), tinnitus (37.55%), gum bleeding (33.3%), joint pain (31.2%), eye infl ammation (25%), leg cramps (25%), palpable lymph nodes (25%), chest pain (18.8%), skin lesions (16.7%), abnormal vision (8.3%). Conclusion: Most of the diseases were due to poor health habits. It was found that the nature of work, their life styles and the different types of behaviour they adapt finally lead them to many health problems. The health problem can be prevented, if an integrated program that involves all the issues are developed and implemented. Key words: Street children; health problems; Dharan. DOI: 10.3126/kumj.v7i3.2737 Kathmandu University Medical Journal (2009) Vol.7, No.3 Issue 27, 272-279

A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition

Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM is a sequential generative deep learning inherited from Recurrent Neural Network (RNN). SCCRFs is a distinctive feature of conditional random field (CRF) that can represent long term dependencies. In the first phase of the proposed approach, we recognized the concurrent activities using the BiLSTM technique, and in the second phase, SCCRF identifies the interleaved activity. Accuracy of the proposed framework against the counterpart state-of-art methods using the publicly available datasets in a smart home environment is analyzed. Our experiment's result surpasses the previously proposed approaches with an average accuracy of more than 93%.

Search for Heavy Neutral Leptons in Events with Three Charged Leptons in Proton-Proton Collisions at sqrt[s]=13 TeV

A search for a heavy neutral lepton N of Majorana nature decaying into a W boson and a charged lepton is performed using the CMS detector at the LHC. The targeted signature consists of three prompt charged leptons in any flavor combination of electrons and muons. The data were collected in proton-proton collisions at a center-of-mass energy of 13 TeV, with an integrated luminosity of 35.9  fb^{-1}. The search is performed in the N mass range between 1 GeV and 1.2 TeV. The data are found to be consistent with the expected standard model background. Upper limits are set on the values of |V_{eN}|^{2} and |V_{μN}|^{2}, where V_{ℓN} is the matrix element describing the mixing of N with the standard model neutrino of flavor ℓ. These are the first direct limits for N masses above 500 GeV and the first limits obtained at a hadron collider for N masses below 40 GeV.

Epileptic-Net: An Improved Epileptic Seizure Detection System Using Dense Convolutional Block with Attention Network from EEG

Epilepsy is a complex neurological condition that affects a large number of people worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and is widely used in epilepsy diagnosis, but it usually requires manual inspection, which can be hours long, by a neurologist. Several automatic systems have been proposed to detect epilepsy but still have some unsolved issues. In this study, we proposed a dynamic method using a deep learning model (Epileptic-Net) to detect an epileptic seizure. The proposed method is largely heterogeneous and comprised of the dense convolutional blocks (DCB), feature attention modules (FAM), residual blocks (RB), and hypercolumn technique (HT). Firstly, DCB is used to get the discriminative features from the EEG samples. Then, FAM extracts the essential features from the samples. After that, RB learns more vital parts as it entirely uses information in the convolutional layer. Finally, HT retains the efficient local features extracted from the layers situated at the different levels of the model. Its performance has been evaluated on the University of Bonn EEG dataset, divided into five distinct classes. The proposed Epileptic-Net achieves the average accuracy of 99.95% in the two-class classification, 99.98% in the three-class classification, 99.96% in the four-class classification, and 99.96% in classifying the complicated five-class problem. Thus the proposed approach shows more competitive results than the existing model to detect epileptic seizures. We also hope that this method can support experts in achieving objective and reliable results, lowering the misdiagnosis rate, and assisting in decision-making.

Measurement of prompt and nonprompt charmonium suppression in PbPb collisions at 5.02 TeV

The nuclear modification factors of J / ψ and ψ (2S) mesons are measured in PbPb collisions at a centre-of-mass energy per nucleon pair ofs NN = 5.02 TeV . The analysis is based on PbPb and p p data samples collected by CMS at the LHC in 2015, corresponding to integrated luminosities of 464μ b - 1 and 28pb -1 , respectively. The measurements are performed in the dimuon rapidity range of | y | < 2.4 as a function of centrality, rapidity, and transverse momentum ( p T ) fromp T = 3 GeV / c in the most forward region and up to 50GeV / c . Both prompt and nonprompt (coming from b hadron decays) J / ψ mesons are observed to be increasingly suppressed with centrality, with a magnitude similar to the one observed ats NN = 2.76 TeV for the two J / ψ meson components. No dependence on rapidity is observed for either prompt or nonprompt J / ψ mesons. An indication of a lower prompt J / ψ meson suppression atp T > 25 GeV / c is seen with respect to that observed at intermediate p T . The prompt ψ (2S) meson yield is found to be more suppressed than that of the prompt J / ψ mesons in the entire p T range.

Search for Narrow Resonances in Dijet Final States at sqrt[s]=8 TeV with the Novel CMS Technique of Data Scouting

A search for narrow resonances decaying into dijet final states is performed on data from proton-proton collisions at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 18.8  fb^{-1}. The data were collected with the CMS detector using a novel technique called data scouting, in which the information associated with these selected events is much reduced, permitting collection of larger data samples. This technique enables CMS to record events containing jets at a rate of 1 kHz, by collecting the data from the high-level-trigger system. In this way, the sensitivity to low-mass resonances is increased significantly, allowing previously inaccessible couplings of new resonances to quarks and gluons to be probed. The resulting dijet mass distribution yields no evidence of narrow resonances. Upper limits are presented on the resonance cross sections as a function of mass, and compared with a variety of models predicting narrow resonances. The limits are translated into upper limits on the coupling of a leptophobic resonance Z_{B}^{'} to quarks, improving on the results obtained by previous experiments for the mass range from 500 to 800 GeV.

Search for new phenomena with the M T 2 variable in the all-hadronic final state produced in proton-proton collisions at s = 13 TeV

A search for new phenomena is performed using events with jets and significant transverse momentum imbalance, as inferred through the M T 2 variable. The results are based on a sample of proton-proton collisions collected in 2016 at a center-of-mass energy of 13TeV with the CMS detector and corresponding to an integrated luminosity of 35.9fb -1 . No excess event yield is observed above the predicted standard model background, and the results are interpreted as exclusion limits at 95% confidence level on the masses of predicted particles in a variety of simplified models of R-parity conserving supersymmetry. Depending on the details of the model, 95% confidence level lower limits on the gluino (light-flavor squark) masses are placed up to 2025 (1550)GeV . Mass limits as high as 1070 (1175)GeV are set on the masses of top (bottom) squarks. Information is provided to enable re-interpretation of these results, including model-independent limits on the number of non-standard model events for a set of simplified, inclusive search regions.

Search for Resonant Production of High-Mass Photon Pairs in Proton-Proton Collisions at sqrt[s]=8 and 13 TeV

A search for the resonant production of high-mass photon pairs is presented. The analysis is based on samples of proton-proton collision data collected by the CMS experiment at center-of-mass energies of 8 and 13 TeV, corresponding to integrated luminosities of 19.7 and 3.3  fb^{-1}, respectively. The interpretation of the search results focuses on spin-0 and spin-2 resonances with masses between 0.5 and 4 TeV and with widths, relative to the mass, between 1.4×10^{-4} and 5.6×10^{-2}. Limits are set on scalar resonances produced through gluon-gluon fusion, and on Randall-Sundrum gravitons. A modest excess of events compatible with a narrow resonance with a mass of about 750 GeV is observed. The local significance of the excess is approximately 3.4 standard deviations. The significance is reduced to 1.6 standard deviations once the effect of searching under multiple signal hypotheses is considered. More data are required to determine the origin of this excess.

Observation of Charge-Dependent Azimuthal Correlations in p-Pb Collisions and Its Implication for the Search for the Chiral Magnetic Effect

Charge-dependent azimuthal particle correlations with respect to the second-order event plane in p-Pb and PbPb collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV have been studied with the CMS experiment at the LHC. The measurement is performed with a three-particle correlation technique, using two particles with the same or opposite charge within the pseudorapidity range |η|<2.4, and a third particle measured in the hadron forward calorimeters (4.4<|η|<5). The observed differences between the same and opposite sign correlations, as functions of multiplicity and η gap between the two charged particles, are of similar magnitude in p-Pb and PbPb collisions at the same multiplicities. These results pose a challenge for the interpretation of charge-dependent azimuthal correlations in heavy ion collisions in terms of the chiral magnetic effect.

Search for dark matter produced in association with heavy-flavor quark pairs in proton-proton collisions at s = 13 TeV

A search is presented for an excess of events with heavy-flavor quark pairs ( t t ¯ and b b ¯ ) and a large imbalance in transverse momentum in data from proton-proton collisions at a center-of-mass energy of 13TeV . The data correspond to an integrated luminosity of 2.2fb - 1 collected with the CMS detector at the CERN LHC. No deviations are observed with respect to standard model predictions. The results are used in the first interpretation of dark matter production in t t ¯ and b b ¯ final states in a simplified model. This analysis is also the first to perform a statistical combination of searches for dark matter produced with different heavy-flavor final states. The combination provides exclusions that are stronger than those achieved with individual heavy-flavor final states.

Measurement of the B^{±} Meson Nuclear Modification Factor in Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV

The differential production cross sections of B^{±} mesons are measured via the exclusive decay channels B^{±}→J/ψK^{±}→μ^{+}μ^{-}K^{±} as a function of transverse momentum in pp and Pb-Pb collisions at a center-of-mass energy sqrt[s_{NN}]=5.02  TeV per nucleon pair with the CMS detector at the LHC. The pp(Pb-Pb) data set used for this analysis corresponds to an integrated luminosity of 28.0  pb^{-1} (351  μb^{-1}). The measurement is performed in the B^{±} meson transverse momentum range of 7 to 50  GeV/c, in the rapidity interval |y|<2.4. In this kinematic range, a strong suppression of the production cross section by about a factor of 2 is observed in the Pb-Pb system in comparison to the expectation from pp reference data. These results are found to be roughly compatible with theoretical calculations incorporating beauty quark diffusion and energy loss in a quark-gluon plasma.