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Bridging the gender, climate, and health gap: the road to COP29
No full textFocusing specifically on the gender–climate–health nexus, this Personal View builds on existing feminist works and analyses to discuss why intersectional approaches to climate policy and inclusive representation in climate decision making are crucial for achieving just and equitable solutions to address the impacts of climate change on human health and societies. This Personal View highlights how women, girls, and gender-diverse people often face disproportionate climate-related health impacts, particularly those who experience compounding and overlapping vulnerabilities due to current and former systems of oppression. We summarise the insufficient meaningful inclusion of gender, health, and their intersection in international climate governance. Despite the tendency to conflate gender equality with number-based representation, climate governance under the UNFCCC (1995–2023) remains dominated by men, with several countries projected to take over a decade to achieve gender parity in their Party delegations. Advancing gender-responsiveness in climate policy and implementation and promoting equitable participation in climate governance will not only improve the inclusivity and effectiveness of national strategies, but will also build more resilient, equitable, and healthier societies
Comodule theories in Grothendieck categories and relative Hopf objects
No full textWe develop the categorical algebra of the noncommutative base change of a comodule category by means of a Grothendieck category S. We describe when the resulting category of comodules is locally finitely generated, locally noetherian or may be recovered as a coreflective subcategory of the noncommutative base change of a module category. We also introduce the category SHA of relative (A,H)-Hopf modules in S, where H is a Hopf algebra and A is a right H-comodule algebra. We study the cohomological theory in SHA by means of spectral sequences. Using coinduction functors and functors of coinvariants, we study torsion theories and how they relate to injective resolutions in SHA. Finally, we use the theory of associated primes and support in noncommutative base change of module categories to give direct sum decompositions of minimal injective resolutions in SHA
Corrigendum to “Second round statewide sentinel-based population survey for estimation of the burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population of Karnataka, India, during January-February 2021” [IJID Regions Vol 1(2021) pages 107–116, (S2772707621000217), (10.1016/j.ijregi.2021.10.008)]
No full textThe authors regret the affiliation correction of Dr. Giridhara R. Babu. The correct affiliation should be amended to read as follows: Giridhara R Babu, Department of Population Medicine, College of Medicine, QU Health, Qatar University. The authors would like to apologise for any inconvenience caused
DeMoS: dense module based gene signature detection through quasi-clique: an application to cervical cancer prognosis
No full textNowadays, cervical cancer is a leading cause of death among women. Determining the gene signature is one of the major issues in bioinformatics. Though many of the methodologies and applications have been given as suggestions in recent literature, efficient techniques, which may be considered complex gene expression profiles, will be able to find out the most relevant signatures required. In the given article, we demonstrate a new framework to find out the dense module-based gene signatures (DeMoS) and their targeting miRNAs using the quasi-clique detection algorithm and discuss their application in the field of prognostic survival studies. We used a cervical cancer data repository with prognostic clinical data to conduct this experiment. At first, we executed the empirical Bayes test by applying the linear model for the microarray method to find out the dysregulated genes, or miRNAs. MiRNA-mediated dysregulated target genes were pulled out of the particular dysregulated miRNAs. After that, we discovered densely co-expressed modules by applying a quasi-clique identification technique. The average correlation coefficient has been computed for each resultant module, and the module that contains the highest correlation was composed as the resultant gene signature (10-gene signatures containing ten genes are as follows: FGF9, FGF18, PPP1R9A, ERBB4, DCDC2, TOX3, ARMC3, DNALI1, RGL3, and ENPP3). After that, we applied 10-fold cross-validation to three common classifiers (SVM, PAM, and random forest) and obtained the AUC. (0.95 for SVM, 0.955 for RF, and 0.955 for PAM) that is better than the state-of-the-art algorithms (Li et al. in Technol Cancer Res Treat 17:1533033818767455, 2017/2018; Huang et al. in Cancer 117(15):3363–3373, 2011). In addition to it, we found eight dysregulated miRNAs that have targeted the gene, as mentioned earlier. At last, we performed a prognosis survival study for the resultant gene signature (i.e., containing the p-value of Cox regression as 4.2e-02). DEMOS is very useful for determining the signature for any of the microarray or RNA-Seq profiles. The code is available at https://github.com/sahasuparna/DeMoS
Discriminative Deep Generalized Dependency Analysis for Multi-View Data
No full textIn recent years, a surging interest is noted for combining the information of multiple views to obtain a joint representation of the given data. In multi-view data analysis, the joint representation should be learned from the given input views in such a way that the view-specific information as well as the cross-view dependency are preserved properly. In the context of cross-view dependency, it is expected that both view-consistency and view-discrepancy are addressed simultaneously. Discriminability of the joint representation is also an important aspect in the classification problem. In this regard, a novel deep learning model is proposed to efficiently encapsulate the underlying data distribution over the space of input views. Considering both consensus and complementary principles, a loss function is introduced, based on the concept of the Hilbert-Schmidt independence criterion, to capture the relevant cross-view information from the given multi-view data. Incorporating the supervised information of sample categories not only enhances the discriminative ability of the model but also allows it to classify the given samples into different categories. An upper bound on the error probability of the proposed deep model is estimated in terms of the model architecture. It facilitates determining the optimal architecture of the proposed model for each database. The proficiency of the model is studied on numerous application domains with reference to several state-of-the-art multi-view classification algorithms
Equivariant cohomology for cyclic groups of square-free order
No full textThe main objective of this paper is to compute RO(G)-graded cohomology of G-orbits for the group G=Cn, where n is a product of distinct primes. We compute these groups for the constant Mackey functor Z̲ and the Burnside ring Mackey functor A̲. Among other results, we show that the groups H̲Gα(S0) are mostly determined by the fixed point dimensions of the virtual representations α, except in the case of A̲ coefficients when the fixed point dimensions of α have many zeros. In the case of Z̲ coefficients, the ring structure on the cohomology is also described. The calculations are then used to prove freeness results for certain G-complexes
Ethnomedicinal use of plant roots: A case study of the Juang tribe of eastern India
No full textThe Juang, a tribal community in Odisha indulges in traditional health care on the basis of locally available resources. This age-old practice of using plant extracts such as roots and leaves to treat various ailments and diseases is associated with the community culture and wisdom. The study examines the use of plant extracts, particularly roots by Juangs for treatment of various communicable and non-communicable diseases. The data were collected through questionnaire-based field survey, interviews, focus group discussion (FGD) and observation method. The information on medicinal plants including their local, scientific and family names, method of medicine preparation, life form, dosage, applications and effects was collected. The study includes roots of 16 plant species belonging to 13 families that are used for the medicinal purpose. We observed that roots from various wild plant species are used for treatment of various diseases and disorders such as jaundice, hypertension, rheumatism, asthma, infertility, nocturnal emission, venereal diseases, etc. Indigenous application of roots with specific dosages is based on cultural norms and value of the community. This knowledge is transferred from one generation to the other through oral tradition under prevailing health culture. The continuity of this practice with great acumen is determined by various factors such as socioeconomic status, education, occupation and ecological adaptation. The relevance of inherited indigenous healing culture needs to be investigated in this area for developing an alternative approach to community health services and public health policy
Evidence of fluvial to marine transition in the Siwalik rocks of the Itanagar area, Arunachal Pradesh, India: Implication for the regional paleogeography
No full textSedimentological study of the Middle Siwalik Subansiri and lower part of the Upper Siwalik, Siji Formation, from the Itanagar area evidence for marine incursions within an established continental depositional setting. The braided fluvial deposit of the Subansiri Formation is overlain by the shallow marine fan or braid delta deposit of the Siji Formation, recording for the first time the fluvial to marine transition from this area. In the Subansiri Formation isolated large cross strata of the bar platform, downcurrent accreting cross sets of bar of supra-platform, bar top low-stage channel scour fills and rare, thinly rippled silty flood plain deposits were recognized. These assemblages constitute 5-12 m thick sheet sandstone bodies stacked up, forming about a km-thick Subansiri sandstone succession. In the overlying Siji Formation, abundant wave-and combined flow-ripples, well-developed hummocky and swaley strata, and brackish water trace fossils (Lingulichnus, Arenicolites) indicate a marine depositional regime. The depositional domains of the Siji Formation include mudstone dominated prodelta, alternating planar and cross bedded mudstone-sandstone units of the lower delta front; channelized, cross stratified pebbly sandstone and conglomerate of delta mouth bars, and fine sandstone units with tidal bundles from shallow coastal embayment. The Shillong Plateau is a unique feature in the eastern Himalaya whose deformation pattern influenced the depositional system and makes it different from the Western Himalaya. Further a comparison is carried out of the distinctive features of the western Himalayan Siwalik with those of the eastern (Itanagar) region and emphasize that two distinct tectono-geomorphic regimes characterized the foreland basin system
First and oldest record of Seebachia bronni, type species of Seebachia (Bivalvia: Astartidae), from the late Tithonian (Jurassic) of Kutch, India, and its palaeobiogeographic and evolutionary implications
No full textThe present study reports the oldest occurrence of Seebachia bronni Krauss, 1850, the type species of the genus Seebachia, within the Astartidae from the ferruginous oolitic bands of the upper Tithonian (Jurassic) of Kutch, western India. Although Pruvostiella (Eoseebachia), previously known as Seebachia (Eoseebachia), has been reported from the same horizon of Kutch, analyses using quantitative-morphological and morphometrical characters reveal a significant difference between Pruvostiella (Eoseebachia) and Seebachia (Seebachia). Seebachia has been reported in South Africa, Tanzania and Madagascar. Thus, the occurrence of S. bronni in India sheds light on a marine connection among these regions during the late Tithonian. The range of S. aff. bronni in the Oxfordian of Madagascar to S. bronni in the Tithonian of India and Early Cretaceous of South Africa may indicate an evolutionary size increase. A specimen of S. bronni from the Valanginian was the largest in body size and is possibly a distinct species
From fuzzy-TOPSIS to machine learning: A holistic approach to understanding groundwater fluoride contamination
No full textFluoride (F−) contamination of groundwater is a prevalent environmental issue threatening public health worldwide and in India. This study targets an investigation into spatial distribution and contamination sources of fluoride in Dhanbad, India, to help develop tailored mitigation strategies. A triad of Multi Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning algorithms {logistic regression (LR), classification and regression tree (CART), Random Forest (RF)}, and classical methods has been undertaken here. Groundwater samples (n = 283) were collected for the purpose. Based on permissible limit (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean fluoride concentration in Safe (0.63 ± 0.02 ppm) and Unsafe (3.69 ± 0.3 ppm) groups differed significantly (t-value = −10.04, p \u3c 0.05). Physicochemical parameters (pH, electrical conductivity, total dissolved solids, total hardness, NO3−, HCO3−, SO42−, Cl−, Ca2+, Mg2+, K+, Na+ and F−) were recorded from samples of each group. The samples from ‘Unsafe group’ showed alkaline pH, the abundance of Na+ and HCO3− ions, prolonged rock water interaction in the aquifer, silicate weathering, carbonate dissolution, lack of Ca2+ and calcite precipitation which together facilitated the F− abundance. Aspatial distribution map of F− contamination was created, pinpointing the “contaminated pockets.” Fuzzy- TOPSIS identified that samples from group Safe were closer to the ideal solution. Among these models, the LR proved superior, achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by CART (69.4 %). This study successfully identified the primary contributors to F− contamination in groundwater and the developed models can help predicting fluoride contamination in other areas. The combination of different methodologies (Fuzzy-TOPSIS, machine learning algorithms, and classical methods) results in a synergistic effect where the strengths of each approach compensate for the limitations of the other