Verifying the mass-metallicity relation in damped Lyman-alpha selected galaxies at 0.1<z<3.2

A scaling relation has recently been suggested to combine the galaxy mass-metallicity (MZ) relation with metallicities of damped Lyman-alpha systems (DLAs) in quasar spectra. Based on this relation the stellar masses of the absorbing galaxies can be predicted. We test this prediction by measuring the stellar masses of 12 galaxies in confirmed DLA absorber - galaxy pairs in the redshift range 0.1<z<3.2. We find an excellent agreement between the predicted and measured stellar masses over three orders of magnitude, and we determine the average offset $\langle C_{[M/H]} \rangle$ = 0.44+/-0.10 between absorption and emission metallicities. We further test if $C_{[M/H]}$ could depend on the impact parameter and find a correlation at the 5.5sigma level. The impact parameter dependence of the metallicity corresponds to an average metallicity difference of -0.022+/-0.004 dex/kpc. By including this metallicity vs. impact parameter correlation in the prescription instead of $C_{[M/H]}$, the scatter reduces to 0.39 dex in log M*. We provide a prescription how to calculate the stellar mass (M*,DLA) of the galaxy when both the DLA metallicity and DLA galaxy impact parameter is known. We demonstrate that DLA galaxies follow the MZ relation for luminosity-selected galaxies at z=0.7 and z=2.2 when we include a correction for the correlation between impact parameter and metallicity.Comment: 15 pages, 6 figures. Major revision. Accepted for publication in MNRA

Motion of a Vector Particle in a Curved Spacetime. I. Lagrangian Approach

From the simple Lagrangian the equations of motion for the particle with spin are derived. The spin is shown to be conserved on the particle world-line. In the absence of a spin the equation coincides with that of a geodesic. The equations of motion are valid for massless particles as well, since mass does not enter the equations explicitely.Comment: 6 pages, uses mpla1.sty, published in MPLA, replaced with corrected typo

Transient Global Amnesia as the First Clinical Symptom for Malignant B-Cell Lymphoma with Central Nervous System Involvement

We present the case of an elderly male who was diagnosed with transient global amnesia (TGA), only to be diagnosed with B-cell lymphoma with central nervous system involvement a few weeks later. This is the first ever case reported in literature with lymphoma presenting as TGA. Literature review and pertinent points regarding high-yield imaging protocol for presumed TGA patients are discussed

The ESO UVES Advanced Data Products Quasar Sample - VI. Sub-Damped Lyman-α\alpha Metallicity Measurements and the Circum-Galactic Medium

The Circum-Galactic Medium (CGM) can be probed through the analysis of absorbing systems in the line-of-sight to bright background quasars. We present measurements of the metallicity of a new sample of 15 sub-damped Lyman-$\alpha$ absorbers (sub-DLAs, defined as absorbers with 19.0 < log N(H I) < 20.3) with redshift 0.584 < $\rm z_{abs}$ < 3.104 from the ESO Ultra-Violet Echelle Spectrograph (UVES) Advanced Data Products Quasar Sample (EUADP). We combine these results with other measurements from the literature to produce a compilation of metallicity measurements for 92 sub-DLAs as well as a sample of 362 DLAs. We apply a multi-element analysis to quantify the amount of dust in these two classes of systems. We find that either the element depletion patterns in these systems differ from the Galactic depletion patterns or they have a different nucleosynthetic history than our own Galaxy. We propose a new method to derive the velocity width of absorption profiles, using the modeled Voigt profile features. The correlation between the velocity width delta_V90 of the absorption profile and the metallicity is found to be tighter for DLAs than for sub-DLAs. We report hints of a bimodal distribution in the [Fe/H] metallicity of low redshift (z < 1.25) sub-DLAs, which is unseen at higher redshifts. This feature can be interpreted as a signature from the metal-poor, accreting gas and the metal-rich, outflowing gas, both being traced by sub-DLAs at low redshifts.Comment: 64 pages, 31 figures, 27 tables. Submitted to MNRA

Massive, Absorption-selected Galaxies at Intermediate Redshifts

The nature of absorption-selected galaxies and their connection to the general galaxy population have been open issues for more than three decades, with little information available on their gas properties. Here we show, using detections of carbon monoxide (CO) emission with the Atacama Large Millimeter/submillimeter Array (ALMA), that five of seven high-metallicity, absorption-selected galaxies at intermediate redshifts, $z \approx 0.5-0.8$, have large molecular gas masses, $M_{\rm Mol} \approx (0.6 - 8.2) \times 10^{10} \: {\rm M}_\odot$ and high molecular gas fractions ($f_{\rm Mol} \equiv \: M_{\rm Mol}/(M_\ast + M_{\rm Mol}) \approx 0.29-0.87)$. Their modest star formation rates (SFRs), $\approx (0.3-9.5) \: {\rm M}_\odot$ yr$^{-1}$, then imply long gas depletion timescales, $\approx (3 - 120)$ Gyr. The high-metallicity absorption-selected galaxies at $z \approx 0.5-0.8$ appear distinct from populations of star-forming galaxies at both $z \approx 1.3-2.5$, during the peak of star formation activity in the Universe, and lower redshifts, $z \lesssim 0.05$. Their relatively low SFRs, despite the large molecular gas reservoirs, may indicate a transition in the nature of star formation at intermediate redshifts, $z \approx 0.7$.Comment: 8 pages, 3 figures; accepted for publication in Astrophysical Journal Letters. Minor changes to match the version in press in ApJ

Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things

The Internet of bio-nano things (IoBNT) is an emerging paradigm employing nanoscale (~1–100 nm) biological transceivers to collect in vivo signaling information from the human body and communicate it to healthcare providers over the Internet. Bio-nano-things (BNT) offer external actuation of in-body molecular communication (MC) for targeted drug delivery to otherwise inaccessible parts of the human tissue. BNTs are inter-connected using chemical diffusion channels, forming an in vivo bio-nano network, connected to an external ex vivo environment such as the Internet using bio-cyber interfaces. Bio-luminescent bio-cyber interfacing (BBI) has proven to be promising in realizing IoBNT systems due to their non-obtrusive and low-cost implementation. BBI security, however, is a key concern during practical implementation since Internet connectivity exposes the interfaces to external threat vectors, and accurate classification of anomalous BBI traffic patterns is required to offer mitigation. However, parameter complexity and underlying intricate correlations among BBI traffic characteristics limit the use of existing machine-learning (ML) based anomaly detection methods typically requiring hand-crafted feature designing. To this end, the present work investigates the employment of deep learning (DL) algorithms allowing dynamic and scalable feature engineering to discriminate between normal and anomalous BBI traffic. During extensive validation using singular and multi-dimensional models on the generated dataset, our hybrid convolutional and recurrent ensemble (CNN + LSTM) reported an accuracy of approximately ~93.51% over other deep and shallow structures. Furthermore, employing a hybrid DL network allowed automated extraction of normal as well as temporal features in BBI data, eliminating manual selection and crafting of input features for accurate prediction. Finally, we recommend deployment primitives of the extracted optimal classifier in conventional intrusion detection systems as well as evolving non-Von Neumann architectures for real-time anomaly detection

Flexural behavior of cold-formed steel concrete composite beams

Flexural behavior of thin walled steel-concrete composite sections as cross sections for beams is investigated by conducting an experimental study supported by applicable analytical predictions. The experimental study consists of testing up to failure, simply supported beams of effective span 1440 mm under two point loading. The test specimens consisted of composite box and channel (with lip placed on tension side and compression side) sections, the behavior of which was compared with companion empty sections. To understand the role of shear connectors in developing the composite action, some of the composite sections were provided with novel simple bar type and conventional bolt type shear connectors in the shear zone of beams. Two RCC beams having equivalent ultimate moment carrying capacities as that of composite channel and box sections were also considered in the study. The study showed that the strength to weight ratio of composite beams is much higher than RCC beams and ductility index is also more than RCC and empty beams. The analytical predictions were found to compare fairly well with the experimental results, thereby validating the applicability of rigid plastic theory to cold-formed steel concrete composite beams

Nalidixic acid screening test in detection of decreased fluoroquinolone susceptibility in salmonella typhi isolated from blood

OBJECTIVE: To determine the validity of nalidixic acid screening test in the detection of high MICs of fluoroquinolone against Salmonella(S.) typhi isolated from blood and correlate zone diameters of ofloxacin with that of MIC value for nalidixic acid sensitive and resistant strains. DESIGN: Cross-sectional analytical study. PLACE AND DURATION OF STUDY: Clinical Microbiology Laboratory of the Aga Khan Hospital, Karachi from January 2002 to December 2003.METHODS: Two hundred S. typhi isolates from blood were included for nalidixic acid screening and ofloxacin susceptibility. Antibiotic susceptibilities for both the antibiotics were obtained by disc diffusion method whereas MICs were determined by standard agar dilution method as recommended by NCCLS guidelines. Sensitivity, specificity and correlation between both antimicrobial susceptibility methods were calculated and results expressed as scattergrams.RESULTS: The results broadly classify S. typhi isolates into nalidixic acid resistant strains with no zone of inhibition around 30 mug nalidixic acid disc and nalidixic acid sensitive strains with mean zone of inhibition of 24.9 mm. All S. typhi isolates with ofloxacin MIC of capital ZHE, Cyrillic 0.125 microg/ml were found to be nalidixic acid resistant (MIC capital ZHE, Cyrillic32 microg/ml) whereas the isolates with ofloxacin MIC 0.06 microg/ml were nalidixic acid sensitive (MIC 8 microg/ml). Screening for nalidixic acid resistance was found to be 100% sensitive and 97% specific in identifying S. typhi strains with reduced susceptibility to fluoroquinolone (MIC capital ZHE, Cyrillic 0.125 microg/ml).CONCLUSION: Nalidixic acid resistance as a screening method is proved to be significant in identifying S. typhi isolates with reduced susceptibility to fluoroquinolones. It is also suggested that inhibition zone of 25 mm around 5 microg ofloxacin disc is appropriate as a selection criterion to detect S. typhi isolates with reduced susceptibility to fluoroquinolones

Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio