Miracle-dz/UniFAR-FTUs huggingface dataset:
Occurrence of ESBL producing Yersinia enterocolitica among companion animals in Wayanad district

Pet associated zoonotic diseases is one of the unexplored sector and Yersinia spp. especially antibiotic resistant strains can be a major public health. Hence, the present study was envisaged to evaluate the occurrence and characterization of ESBL producing Y. enterocolitica from dogs and cats of Wayanad district. A total of 100 faecal samples/swabs from dogs (50) and cats (50) were analyzed through conventional culture and molecular method using 16S rRNA PCR-RFLP. The occurrence of Y. enterocolitica was recorded in 12 samples. Species specific-PCR revealed that 12 per cent of samples were identified as Y. enterocolitica (ystA). Further, characterization of isolates for ESBL production through phenotypic disc diffusion method revealed that 41.66% of Y. enterocolitica isolates were ESBL producers and on genotypic PCR assay it was observed that 33.33% of isolates in dogs were found to harbour bla TEM gene. Likewise, 50% of Yersinia isolates (each) recovered from dogs were found to harbour bla SHV and bla CTX-M ESBL genes. Thus, the present study signifies the importance of virulent Yersinia strain harbouring ESBL resistance genes and its dissemination in companion animals which can have the potential to transfer to man.
Diverging Emerging Field of Multi-Task Reinforcement Learning

Multi-Task Reinforcement Learning or MTRL is an emerging field that is gaining tremendous attention from researches since the use of Deep Reinforcement Learning. In this report, we will motivate the need for Multi-Task Reinforcement Learning & see how modern researches are different in many aspects. We will formalize the notion of a task in an attempt to unify formal definitions of different works & provide many domains that have been used in several notable ones. We will answer some questions about the MTRL field, like knowledge about the task to the agent, works using model-free and model-based settings, online and offline model updates & how reuse of experience has been adapted for MTRL. We will then discuss three notable works that differ in approach & the definition of the task and alongside visit some other relevant literature in brief. We will cover Meta-Learning, Hierarchical RL & Adoption of special normalization function with a distributed RL setting as our representative selection of works to review. We will then highlight some of the important challenges faced by MTRL algorithms like Task Interference, Distraction Dilemma and Scalability & how various works have addressed them. We will conclude our report & note some possible future directions.
Deadly Politics: Political Connections, Intergovernmental Transfers, and Mortality

Intergovernmental transfers finance most local government spending in developing and developed economies. The redistribution of government revenues, however, might be subject to distortions for political reasons. I ask whether political connections between heads of the central and subnational governments cause distortions in the allocation of transfers sufficiently large to affect individuals in local government jurisdictions. I exploit close elections within a regression discontinuity design and find that municipalities that become connected to the federal government through the election of a co-partisan of the president receive more transfers, spend more, and have 16% fewer deaths in the two years that follow local elections. Political connections cause distortions in local government capital expenditure equivalent to 17% of all local governments’ investment expenses. I find decreases in mortality from parasitic diseases, homicide, suicide, and transport accidents, suggesting that increased spending in several functions of government, besides health spending, is responsible for the decrease in overall mortality. This is supported by the finding that connected municipalities have lower health operating expenditures and have lower hospital admission rates. I find evidence of a decreasing, convex relationship between mortality and government spending, suggesting that the reduction in deaths in connected municipalities does not compensate for the increase in deaths in unconnected municipalities. JEL: D72, H51, H72, H73, H75, H77, I15
Discrete Element Simulations of Nanoparticles Synthesis in Wet-operating Stirred Media: Effect of the Particle Material

An increasing number of applications from every branch of science and engineering are relying on the use of nanomaterials due to their peculiar properties with respect to the bulk counterpart and the potential of their employment, for example in the environmental field. Hence, there is a growing need to develop alternative strategies to produce such materials, providing first-rate performances and a fine control over the product specifications through safer and more sustainable processes.Here we focus on a low-energy magnetically driven wet milling technique for the synthesis of metal nanoparticles starting from a bulky solid, as a simple, cheap, and sustainable approach providing numerous advantages, including the minimization of nanoparticle air dispersion and greater control over the final product.We exploit discrete element method simulations to investigate the interactions among the grinding beads and the magnetic stirrer, providing information on the frequency and energy of collisions under various operating conditions, in an attempt to highlight the role of source material in the dynamics of the system. The relation of such data with the properties of the produced nanoparticles allows a fine tuning of the process parameters.
Pro Bono as a Human Capital Screening Mechanism in Law Firms

Pro bono legal work has become a common practice at many US law firms. Yet the strategic value of pro bono work has not been wellestablished. A common explanation for why firms take on other types of socially responsible activities is that employees gain information from these activities about aspects of firm quality that are otherwise hard to observe. However, we find little support of this in the the legal services industry, where pro bono is the most important socially responsible activity. We propose a different mechanism for how pro bono influences human capital in law firms: a pro bono case enables the firm to gain information about an aspect of employee quality future employee quality in a more senior role. Indeed, a key feature of pro bono legal work is that it provides an opportunity for junior lawyers to take on roles typical of partners in forprofit cases. Based on a formal screening model and primary evidence, we hypothesize that firms engage in more pro bono activity when they have a higher ratio of equity partners to junior lawyers. Using a panel dataset of the top 200 law firms from 1998-2012, we provide empirical results that support this hypothesis.
Efficacy of organic and inorganic amendments on Sulphur availability in relation to growth and yield of rice in an Alfisol soil

A greenhouse experiment was conducted in the department of Agricultural Chemistry and Soil Science, BCKV Nadia, West-Bengal during rabi season to study the efficacy of organic and inorganic amendments on S availability of rice crop in Alfisol soil collected from rice growing lowland cultivated field located in Jhargram Wet Bengal. Six kilograms of air dry processed soils were taken in each of the 10 kg capacity plastic pots. Farm yard manure (@ 7.5 t ha) was incorporated 15 days before transplanting of rice seedlings (CV. Shatabdi) as per treatment. FertilizersSilicon (50 kg ha) and phosphorus (50 kg ha) were applied during transplanting of rice seedlings as basal. The experiment was laid out in completely Randomized Design (CRD) with three replications and eight treatments involving 24 pots in total viz., Si0P0OM0 (Without any amendment), Si1 (Silicon @ 50 kg ha), P1 (Phosphorus @ 50 kg ha), OM1 (Organic manure @ 7.5 t ha), Si1P1 (Silicon @ 50 kg ha + Phosphorus @ 50 kg ha), Si1OM1 (Silicon @ 50 kg ha + Organic manure @ 7.5 t ha), P1OM1 (Phosphorus @ 50 kg ha+ Organic manure @ 7.5 t ha) and Si1P1OM1 (Silicon @ 50 kg ha + Phosphorus @ 50 kg ha + Organic manure @ 7.5 t ha). Pots were irrigated with water as and when required. The result showed that interaction effect of P, Si and FYM on the plant height (cm) and number of tillers hill was highest under the treatment OM0P1Si1 i.e. 123.2 and 7 compared to control treatment. But the number of panicles hill recorded was the highest under treatment OM0P1Si0 (5.68) in comparison to control. The recorded Grain and straw yield (gpot) and total sulphur uptake (mgpot) were the highest in the treatment OM1P0Si0 (15.87, 16.46 and 9.46, respectively). The concentration of sulphur in rice grain and straw (mgkg) was highest in the treatment OM1P1Si1 (323.3 and 325.1, respectively) comparison to control treatment. Combined application of FYM, silicon and phosphatic fertilizer results in increased S availability, increased grain and straw yields and S uptake by rice in Alfisol soil.
Decreased incidence of preterm birth during COVID-19 pandemic

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Role of Asymptomatic COVID-19 Cases in Viral Transmission: Findings From a Hierarchical Community Contact Network Model

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. Management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing; when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic. Note to Practitioners—This article addresses the urgent need to assess the risk of another COVID-19 outbreak caused by asymptomatic cases and to find the optimal, most practical approach to asymptomatic case management. Previous studies mostly focused on the clinical and statistical characteristics of asymptomatic cases; few have evaluated the impact of asymptomatic case measures using mathematical modeling at the community scale. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) propagation model based on local community structures and social contact networks, according to the development characteristics and trend of COVID-19 in a Chinese community. The conclusion provides theoretical support for emergency work of relevant departments in different periods of an epidemic. In the early stages of the epidemic, timely detection and isolation of symptomatic patients should be a priority. Where there are surplus resources for epidemic prevention, the authorities should consider increasing the proportion of asymptomatic patients being traced. Epidemic prevention measures among family members should be a primary focus of attention. This combination of strategies can help reduce the rate of viral transmission and result in extinguishing the epidemic.
A reduction approach to silting objects for derived categories of hereditary categories

Let $\mathcal{H}$ be a hereditary abelian category over a field $k$ with finite dimensional $\operatorname{Hom}$ and $\operatorname{Ext}$ spaces. It is proved that the bounded derived category $\mathcal{D}^b(\mathcal{H})$ has a silting object iff $\mathcal{H}$ has a tilting object iff $\mathcal{D}^b(\mathcal{H})$ has a simple-minded collection with acyclic $\operatorname{Ext}$-quiver. Along the way, we obtain a new proof for the fact that every presilting object of $\mathcal{D}^b(\mathcal{H})$ is a partial silting object. We also consider the question of complements for pre-simple-minded collections. In contrast to presilting objects, a pre-simple-minded collection $\mathcal{R}$ of $\mathcal{D}^b(\mathcal{H})$ can be completed into a simple-minded collection iff the $\operatorname{Ext}$-quiver of $\mathcal{R}$ is acyclic.
Clinical judgement of General Practitioners for the diagnosis of dementia

Background: The accuracy of General Practitioners' (GPs') clinical judgement for diagnosing dementia is uncertain. Aim: Investigate the accuracy of GPs' clinical judgement for the diagnosis of dementia. Design and Setting: Diagnostic test accuracy study, recruiting from 21 practices around Bristol. Method: The clinical judgement of the treating GP (index test) was based on the information immediately available at their initial consultation with a person aged over 70 years who had symptoms of possible dementia. The reference standard was an assessment by a specialist clinician, based on a standardised clinical examination and made according to ICD-10 criteria for dementia. Results: 240 people were recruited, with a median age of 80 years (IQR 75 to 84 years), of whom 126 (53%) were men and 132 (55%) had dementia. The median duration of symptoms was 24 months (IQR 12 to 36 months) and the median ACE-III score was 75 (IQR 65 to 87). GP clinical judgement had sensitivity 56% (95% CI 47% to 65%) and specificity 89% (95% CI 81% to 94%). Positive likelihood ratio was higher in people aged 70-79 years (6.5, 95% CI 2.9 to 15) compared to people aged [≥] 80 years (3.6, 95% CI 1.7 to 7.6), and in women (10.4, 95% CI 3.4 to 31.7) compared to men (3.2, 95% CI 1.7 to 6.2), whereas the negative likelihood ratio was similar in all groups. Conclusion: GP judgement is more likely to under identify rather than over identify dementia.
Aerial transmission of SARS-CoV-2 virus (and pathogens in general) through environmental e-cigarette aerosol

We examine the plausibility, scope and risks of aerial transmission of pathogens (including the SARS-CoV-2 virus) through respiratory droplets carried by exhaled e-cigarette aerosol (ECA). Observational and laboratory data suggests considering cigarette smoking and mouth breathing through a mouthpiece as convenient proxies to infer the respiratory mechanics and droplets sizes and their rate of emission that should result from vaping. We model exhaled ECA flow as an intermittent turbulent jet evolving into an unstable puff, estimating for low intensity vaping (practiced by 80-90% of vapers) ECA expirations the emission of 2-230 respiratory submicron droplets per puff a horizontal distance spread of 1-2 meters, with intense vaping possibly carrying hundreds and up to 1000 droplets per puff in the submicron range a distance spread over 2 meters. Bystanders exposed to low intensity expirations from an infectious vaper in indoor spaces (home and restaurant scenarios) face a 1% increase of risk with respect to a ''control case'' scenario defined by exclusively rest breathing without vaping. This relative added risk becomes 5-17% for high intensity vaping, 40-90% and over 200% for speaking or coughing (without vaping). We estimate that disinfectant properties of glycols in ECA are unlikely to act efficiently on pathogens carried by vaping expirations under realistic conditions.
Zero-Shot Learning with Knowledge Enhanced Visual Semantic Embeddings

We improve zero-shot learning (ZSL) by incorporating common-sense knowledge in DNNs. We propose Common-Sense based Neuro-Symbolic Loss (CSNL) that formulates prior knowledge as novel neuro-symbolic loss functions that regularize visual-semantic embedding. CSNL forces visual features in the VSE to obey common-sense rules relating to hypernyms and attributes. We introduce two key novelties for improved learning: (1) enforcement of rules for a group instead of a single concept to take into account class-wise relationships, and (2) confidence margins inside logical operators that enable implicit curriculum learning and prevent premature overfitting. We evaluate the advantages of incorporating each knowledge source and show consistent gains over prior state-of-art methods in both conventional and generalized ZSL e.g. 11.5%, 5.5%, and 11.6% improvements on AWA2, CUB, and Kinetics respectively.
Emergent Road Rules In Multi-Agent Driving Environments

For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules" include rules that drivers are required to follow by law -- such as the requirement that vehicles stop at red lights -- as well as more subtle social rules -- such as the implicit designation of fast lanes on the highway. In this paper, we provide empirical evidence that suggests that -- instead of hard-coding road rules into self-driving algorithms -- a scalable alternative may be to design multi-agent environments in which road rules emerge as optimal solutions to the problem of maximizing traffic flow. We analyze what ingredients in driving environments cause the emergence of these road rules and find that two crucial factors are noisy perception and agents' spatial density. We provide qualitative and quantitative evidence of the emergence of seven social driving behaviors, ranging from obeying traffic signals to following lanes, all of which emerge from training agents to drive quickly to destinations without colliding. Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.
Measurement of proton quenching in a plastic scintillator detector

The non-linear energy response of the plastic scintillator EJ-260 is measured with the MicroCHANDLER detector, using neutron beams of energy 5 to 27 MeV at the Triangle Universities Nuclear Laboratory. The first and second order Birks' constants are extracted from the data, and found to be kB = (8.70 ± 0.93)× 10−3 g/cm2/MeV and kC = (1.42 ± 1.00) × 10−5 (g/cm2/MeV)2. This result covers a unique energy range that is of direct relevance for fast neutron backgrounds in reactor inverse beta decay detectors. These measurements will improve the energy non-linearity modeling of plastic scintillator detectors. In particular, the updated energy response model will lead to an improvement of fast neutron modeling for detectors based on the CHANDLER reactor neutrino detector technology.
Deep Learning Approach to Channel Sensing and Hybrid Precoding for TDD Massive MIMO Systems

This paper proposes a deep learning approach to channel sensing and downlink hybrid analog and digital beamforming for massive multiple-input multiple-output systems with a limited number of radio-frequency chains operating in the time-division duplex mode at millimeter frequency. The conventional downlink precoding design hinges on the two-step process of first estimating the high-dimensional channel based on the uplink pilots received through the channel sensing matrices, then designing the precoding matrices based on the estimated channel. This two-step process is, however, not necessarily optimal, especially when the pilot length is short. This paper shows that by designing the analog sensing and the downlink precoding matrices directly from the received pilots without the intermediate channel estimation step, the overall system performance can be significantly improved. Specifically, we propose a channel sensing and hybrid precoding methodology that divides the pilot phase into an analog and a digital training phase. A deep neural network is utilized in the first phase to design the uplink channel sensing and the downlink analog beamformer. Subsequently, we fix the analog beamformers and design the digital precoder based on the equivalent low-dimensional channel. A key feature of the proposed deep learning architecture is that it decomposes into parallel independent single-user DNNs so that the overall design is generalizable to systems with an arbitrary number of users. Numerical comparisons reveal that the proposed methodology requires significantly less training overhead than the channel recovery based counterparts, and can approach the performance of systems with full channel state information with relatively few pilots.
A Population-Based Hybrid Approach for Hyperparameter Optimization of Neural Networks

Hyperparameter optimization is a fundamental part of Auto Machine Learning (AutoML) and it has been widely researched in recent years; however, it still remains as one of the main challenges in this area. Motivated by the need of faster and more accurate hyperparameter optimization algorithms we developed HyperBRKGA, a new population-based approach for hyperparameter optimization. HyperBRKGA combines the Biased Random Key Genetic Algorithm with an Exploitation Method in order to search the hyperparameter space more efficiently than other commonly used hyperparameter optimization algorithms, such as Grid Search, Random Search, CMA-ES or Bayesian Optimization. We develop and test two different alternatives for this Exploitation Method: Random Walk and Bayesian Walk. We also discuss and implement other schemes, such as a Training Data Reduction Strategy and a Diversity Control strategy, in order to further improve the efficacy of our method. We performed several computational experiments on 8 different datasets to assess the effectiveness of the proposed approach. Results showed that HyperBRKGA could find hyperparameter configurations that outperformed in terms of predictive quality the baseline methods in 6 out of 8 datasets while showing a reasonable execution time. Lastly, we conducted an ablation study and showed that the addition of every component was relevant to achieving high quality results.