A quantitative analysis of the FIV and HIV genome using bioinformatics software

Among viruses, the human immunodeficiency virus (HIV) presented the greatest challenge to human kinds. the HIV and FIV gag genome was sequenced using the Illumina MiSeq Benchtop next-generation sequencer.The DNA sequences obtained were then run through the LALIGN bioinformatics software to compute the E value, bit score, waterman eggert score, percent identity,which are four important indicators of how similar the sequences are. The E value was 3.1 x 10 ^-9, the percent identity was 54.4 percent and the bit score was 51.9. It was also sensed that base 1600 to 1990 in HIV and base 800 to 910 in FIV have a higher than normal similarity. This reflects that while the DNA sequences of the gag region of both the HIV and FIV genome are rather similar and it is unlikely that this similarity is due to random chance, there are a noticeable amount of differences. A better understanding of the level of similarity and differences in the gag region of the genome sequence would facilitate our understanding of structural and cellular behavioral differences between FIV and HIV, and in the long term it prevides new explanations to differences observed in previous studies, or even facilitate the development of an effective HIV treatment.

Classification revisited: On time, methodology and position in decolonizing anthropology

Renewed calls for decolonizing anthropology in the 21st century raise the question of what work earlier waves of decolonization since the 1960s have left undone. Some of this work should focus on the classification of human differences, which figured prominently in all phases of the discipline’s history: as a methodology in its racist phases, as an object of study during its late colonial phase of professionalization, as self-critical reflexivity in the 1980s and 1990s, and as a renewed critique in the 21st century. Can a universal methodology of studying classifications of human kinds arise from the discipline’s past of colonial stereotyping? I argue affirmatively, through an approach that recognizes time as the epistemic condition that connects past and present positions to present and future methodologies. Firstly, my analysis distinguishes the parochial embedding in colonial culture of Durkheim and Mauss’ ideas about classification from their more universal intentions. This is then developed into a threefold reflexive and timeful methodology of studying classification’s nominal-descriptive, constructive, and interventionist dimensions—a process of adding temporality to the study of classification. Subsequently, Anténor Firmin’s 19th-century critique of racial classifications, and W. E. B. Du Bois’s theory of double consciousness help to show how this threefold methodology addresses the insufficiently theorized process of being classified and discriminated against through racial categories wielded by the powers that be. These arguments radicalize the essay’s timeful perspective by concluding that we need to avoid modernist uses of time as classification and adopt the aforementioned threefold methodology in order to put time in classifications of human kinds. This reverses modern positivism’s subordination to methodological rules of the epistemic conditions posed by contingent history and shows instead that the universal goals of methodology should be understood as a future ideal.

Identifying the Explanatory Domain of the Looping Effect: Congruent and Incongruent Feedback Mechanisms of Interactive Kinds

Ian Hacking uses the looping effect to describe how classificatory practices in the human sciences interact with the classified people. While arguably this interaction renders the affected human kinds unstable and hence different from natural kinds, realists argue that also some prototypical natural kinds are interactive and human kinds in general are stable enough to support explanations and predictions. I defend a more fine-grained realist interpretation of interactive human kinds by arguing for an explanatory domain account of the looping effect. First, I argue that knowledge of the feedback mechanisms that mediate the looping effect can supplement, and help to identify, the applicability domain over which a kind and its property variations are stably explainable. Second, by applying this account to cross-cultural case studies of psychiatric disorders, I distinguish between congruent feedback mechanisms that explain matches between classifications and kinds, and incongruent feedback mechanisms that explain mismatches. For example, congruent mechanisms maintain Western auditory experiences in schizophrenia, whereas exporting diagnostic labels inflicts incongruence by influencing local experiences. Knowledge of the mechanisms can strengthen explanatory domains, and thereby facilitate classificatory adjustments and possible interventions on psychiatric disorders.

Toward a Comparative History of Racial Thought in Africa: Historicism, Barbarism, Autochthony

Using material from the history of African thought, this essay proposes a strategy for writing a comparative history of race that ranges beyond a consideration of white supremacy and its anti-racist inflections. Studies of race outside the global north have often been hobbled by rigid modernist assumptions that over-privilege the determining influence of Western discourses at the expense of local intellectual inheritances. This essay, in contrast, proposes a focus on locally inherited discourses of difference that have shown signs of becoming racialized, at times through entanglement with Western ideas. It pays particular attention to discourses that arranged “human kinds” along a progression from barbarian to civilized, suggesting the presence of African historicisms that in modern times have converged with the stadial ideas that played a major role in Western racial thought.

Study on Ai Performance and Possibilities for Future Wars

Objective: Too much reliance is being laid on applications of AI in finding solution to various problems being faced by human kinds. The recent outbreak of COVID 19 is comparable to outbreak of any other havoc on the mankind including war, due to similar characteristics of three Vs i.e. Velocity; Volume and Veracity. The objective of study is to draw out lessons from AI applications and success achieved during pandemic spread for the application of AI in future wars. Method : The study has been done in two parts first part consist of descriptive analysis of performance of AI in predicting, diagnosing and finding the resolution to current pandemic. The second part consists of working out the way forward for application of AI during future warfare. The methodology used for working out expected Ai performance during future warfare has been survey and interaction with the experts in the field. Findings :The findings of the study have been that AI has been quite a success story during prediction and diagnosis stage of Pandemic but the actual performance in finding solution to the problem has been questionable. As a result the countries world over have suffered huge losses and had to resort to various preventions measures. Novelty : The novelty of the study is derived from the fact that various study in the AI field have been specific to a particular field and no work was done on learning the lessons from one domain to its application to other field of similar nature. The question of “How much reliance should be laid upon AI during a future war.” has largely remained unanswered. With reference to ethical and legal issues related to AI in employment of Killer Robots the study recommends future course of action. We need to ensure that no rogue application of AI takes place in a sector where the very existence of humanity is at stake.