A generative adversarial strategy for modeling relation paths in knowledge base representation learning

Enabling neural networks to perform multi-hop (mh) reasoning over knowledge bases (KBs) is vital for tasks such as question-answering and query expansion. Typically, recurrent neural networks (RNNs) trained with explicit objectives are used to model mh relation paths (mh-RPs). In this work, we hypothesize that explicit objectives are not the most effective strategy effective for learning mh-RNN reasoning models, proposing instead a generative adversarial network (GAN) based approach. The proposed model – mh Relation GAN (mh-RGAN) – consists of two networks; a generator $G$, and discriminator $D$. $G$ is tasked with composing a mh-RP and $D$ with discriminating between real and fake paths. During training, $G$ and $D$ contest each other adversarially as follows: $G$ attempts to fool $D$ by composing an indistinguishably invalid mh-RP given a head entity and a relation, while $D$ attempts to discriminate between valid and invalid reasoning chains until convergence. The resulting model is tested on benchmarks WordNet and FreeBase datasets and evaluated on the link prediction task using MRR and HIT@ 10, achieving best-in-class performance in all cases

In-vitro Antibacterial, Antifungal and cytotoxic activity of cobalt (II), copper (II), nickel (II) and zinc (II) complexes with furanylmethyl- and thienylmethyl-dithiolenes: [1, 3-dithiole- 2-one and 1,3-dithiole-2-thione]

Some antibacterial and antifungal furanylmethyl-and thienylmethyl dithiolenes and, their Co(II), Cu(II), Ni (II) and Zn (II) complexes have been synthesized, characterized and screened for their in vitro antibacterial activity against four Gram-negative; Escherichia coli, Pseudomonas aeruginosa, Salmonella typhi and Shigella flexeneri, and two Gram-positive; Bacillus subtilis and Staphylococcus aureus bacterial strains, and for in-vitro antifungal activity against Trichophyton longifusus, Candida albicans, Aspergillus flavus, Microsporum canis, Fusarium solani and Candida glaberata. All compounds showed significant antibacterial and antifungal activity. The metal complexes, however, were shown to possess better activity as compared to the simple ligands. The brine shrimp bioassay was also carried out to study their in-vitro cytotoxic properties

Ethnic inequality, multimorbidity and psychosis: can a syndemic framework resolve disputed evidence?

Syndemic theory is described as population-level clustering or co-occurrence of health conditions in the context of shared aetiologies that interact and can act synergistically. These influences appear to act within specific places of high disadvantage. We suggest ethnic inequality in experiences and outcomes of multimorbidity, including psychosis, may be explained through a syndemic framework. We discuss the evidence for each component of syndemic theory in relation to psychosis, using psychosis and diabetes as an exemplar. Following this, we discuss the practical and theoretical adaptations to syndemic theory in order to apply it to psychosis, ethnic inequality and multimorbidity, with implications for research, policy, and practice

Antibacterial, antifungal and cytotoxic properties of novel N-substituted sulfonamides from 4-hydroxycoumarin

A new series of 4-({[2, 4-dioxo-2H-chromen-3 (4H)-ylidene] methyl} amino) sulfonamides have been obtained by the condensation reaction of 4-hydroxycoumarin with various sulfonamides (sulfanilamide, sulfaguanidine, p-aminomethyl-sufanilamide, p-aminoethylsufanilamide, sulfathiazole, sulfamethoxazole, sulfamethazine and 4-[(2-amino-4-pyrimidinyl) amino] benzenesulfonamide) in the presence of an excess of ethylorthoformate. These compounds were screened for their in-vitro antibacterial activity against four Gram-negative (E. coli, S. flexneri, P. aeruginosa and S. typhi) and two Gram-positive (B. subtilis and S. aureus) bacterial strains and for in-vitro antifungal activity against T. longifusus, C. albicans, A. flavus, M. canis, F. solani and C. glaberata. Results revealed that a significant antibacterial activity was observed by compounds (4) and (5), (6) and (8) against two Gram-negative, (P. aeruginosa and S. typhi) and two Gram-positive (B. subtilis and S. aureus) species, respectively. Of these (4) was found to be the most active. Similarly, for antifungal activity compounds (3) and (8) showed significant activity against M. canis and, (6) and (8) against F. solani. The brine shrimp bioassay was also carried out to study their in-vitro cytotoxic properties and only two compounds, (4) and (8) possessing LD50 = 2.9072 x 10(-4) and 3.2844 x 10(-4) M, respectively, displayed potent cytotoxic activity against Artemia salin

Stumbling Blocks of Online Learning During COVID 19 Pandemic – Perspectives of Students of Selected Universities in London

COVID 19 Pandemic has led to mayhem across the Planet. Educational institutions are the worst affected arena. There is a paradigm shift from conventional classroom teaching to online methods. But it has its own obstructions. Thus, this research is undertaken to study the impediments of online learning faced by the students of selected universities of London. The questionnaire was administered among 200 students out of which 196 responded. The results of the Study reveal that the major obstructions which hindered online learning were lack of computer skills, internet connectivity issues, difficulty in operating the software, absence of social bonding between teachers and students, difficulty in recording lectures, difficulty in grasping practical courses such as mathematics, finance, accounting, engineering etc. To cope up with the Stumbling Blocks, the Study advocates some of the most innovative and creative ways such as application of Bloom’s Digital Taxonomy, VARK Model, 5/5/5 rule etc

Mathematical Modeling of Radiotherapy: Impact of Model Selection on Estimating Minimum Radiation Dose for Tumor Control

INTRODUCTION: Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose. METHODS: In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values. RESULTS: Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC. DISCUSSION: Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations

Multi-hazard groundwater risks to water supply from shallow depths: challenges to achieving the sustainable development goals in Bangladesh

Background: Groundwater currently provides 98% of all drinking-water supply in Bangladesh. Groundwater is found throughout Bangladesh but its quality (i.e. arsenic and salinity contamination) and quantity (i.e., water-storage depletion) vary across hydrological environments, posing unique challenges to certain geographical areas and population groups. Yet, no national-scale, multi-parameter groundwater hazard maps currently exist enabling water resources managers and policy makers to identify vulnerable areas to public health. Methods: We develop, for the first time, groundwater multi-hazard maps at the national scale of Bangladesh combining information on arsenic, salinity and water storage. We apply geospatial techniques in ‘R’ programming language and ArcGIS environment, linking hydrological indicators for water quality and quantity to construct risk maps. A range of socio-economic variables including access to drinking and irrigation water supplies and social vulnerability (i.e., poverty) are overlaid on these risk maps to estimate exposures. Results: Our multi-parameter groundwater hazard maps show that a considerable proportion of land area (5% to 24% under extremely-high to high risks) in Bangladesh is currently under combined risk of arsenic and salinity contamination, and groundwater-storage depletion. As small as 6.5 million (2.2 million poor) to 24.4 million (8.6 million poor) people are exposed to a combined risk of high arsenic, salinity and groundwater-storage depletion. Conclusions: Our groundwater hazard maps reveal areas and exposure of population groups to water risks posed by arsenic and salinity contamination and depletion of water storage. These geospatial hazard maps can potentially guide policymakers in prioritizing mitigation and adaptation measures in order to achieve the United Nation’s Sustainable Development Goals across the water, agriculture and public health sectors in Bangladesh

Math meets the Clinic: Modeling Patient Specific HNSCC Radiation Response Dynamics

Head and neck squamous cell carcinomas (HNSCCs) originate from the mucosal lining of the upper aerodigestive tract and radiation therapy has become a fundamental component in the standard care for these patients. However, the treatment\u27s unique dynamics can lead to disparities in outcomes and biological responses in both tumor and normal tissues. A significant challenge remains in predicting individual patient responses to radiation, with variability often resulting in under or over-treatment and potentially adverse effects. The absence of reliable biomarkers highlights the need for predictive measures to guide clinical decisions in real-time. The integration of mathematical models in radiation therapy offers a promising solution, with models incorporating a global lambda demonstrating the ability to predict treatment responses beyond initial weeks. These models provide a framework to address patient response variability, potentially improving survival rates and quality of life.https://openworks.mdanderson.org/radonc24/1003/thumbnail.jp

Understanding psychosis complexity through a syndemic framework: A systematic review.

Psychotic conditions pose significant challenges due to their complex aetiology and impact on individuals and communities. Syndemic theory offers a promising framework to understand the interconnectedness of various health and social problems in the context of psychosis. This systematic review aims to examine existing literature on testing whether psychosis is better understood as a component of a syndemic. We conducted a systematic search of 7 databases, resulting in the inclusion of five original articles. Findings from these studies indicate a syndemic characterized by the coexistence of various health and social conditions, are associated with a greater risk of psychosis, adverse health outcomes, and disparities, especially among ethnic minorities and deprived populations. This review underscores the compelling need for a new paradigm and datasets that can investigate how psychosis emerges in the context of a syndemic, ultimately guiding more effective preventive and care interventions as well as policies to improve the health of marginalised communities living in precarity