Will the Role of Infrastructure in Higher Education Become Redundant?-A Critical Study of the Expectation of Students

During the countrywide lockdown due to the pandemic, students attended online classes and it appeared that physical classes may not be necessary or lose their relevance in future as students could easily get access to online learning materials. Students sitting in the comfort of their homes could access the best learning materials available anywhere in the world and excel in their respective fields of study. One of the astounding questions that came for debate those days was whether we need huge college buildings and enormous infrastructure facilities when laptop, a mobile phone or any device serves the purpose. But interestingly, students slowly but steadily showed signs of boredom and ennui with the online classes and were eager to join the physical classes. Many of them went to the extent of demanding physical classes. This phenomenon has led to some of the significant questions that we, as educationists and policy makers need to seriously address with immediate priority.First and foremost, why is that students prefer physical classes in spite of the fact that best of the resource material is available on their finger tips? What are the expectations of students in the changed contemporary scenario? Will the words of Bill Gates on the complete irrelevance of physical campuses, classrooms and physical infrastructure prove to be prophetic?This paper will delve deep into these research questions by conducting an systematic empirical study of the phenomenon. The research partners wouldbe the managers of education, students, staff, parents and other stakeholders.It is envisaged that the outcome of the study will be of immense use in the context of most of the higher education institutions adopting virtual, blended or hybridized pedagogies. They will have tremendous implications to the higher education scenario of the future. Key words: Pandemic, Physical classes, Virtual classrooms, Blended mode, Higher Education DOI: 10.7176/JEP/13-30-03 Publication date:October 31st 202

Knowledge Questionnaire on Home Care of Schizophrenics (KQHS): Validity and Reliability.

The number of questionnaires developed by nurses has increased in recent years. While the rigor applied to the questionnaire development process may be improving, there is a need for those who develop questionnaires to be skillful. This paper reports the process of development of a Knowledge Questionnaire on Home care of Schizophrenics (KQHS) which is a part of a larger study. The KQHS is developed with an aim to determine the knowledge of primary caregivers on home care of schizophrenic patients. It is a self-administered 32 item multiple choice questionnaire that quantifies four aspects of home care, i.e, meaning, cause, signs and symptoms of schizophrenia and care of schizophrenics. Review of literature, preparation of blueprint, development of the items, validity, pretesting and reliability were the steps used in the process of its development. After establishing the content validity, the KQHS was pretested. Split half technique (odd-even) was used to determine coefficient correlation using Karl Pearson formula, following which the Spearman’s Brown Prophecy formula was used for establishing the reliability r (20) =0.92.  Item analysis was computed to assess performance of individual question and it revealed overall good results with value for item difficulty ranging from 20 to 80 percentage and item discrimination index of above 0.2. The KQHS is a brief and simple-to-use instrument, which is valid and reliable. It is suitable for assessing the knowledge on home care of schizophrenic patients among primary caregivers. Keywords: Knowledge Questionnaire, Item analysis, Validity, Reliability, Caregivers, Schizophrenia, Homecare

Theory Summary and Future Directions

Summary talk at the Lepton-Photon Symposium, Cornell University, Aug. 10-15, 1993.Comment: (Talk presented at the Lepton-Photon Symposium, Cornell University, Aug. 10-15, 1993.) 19 page

A Complex Case of Pulmonary Silico-Tuberculosis and Review of Literature

Silicosis caused by the inhalation/deposition of free silica particles is characterized by pulmonary inflammation/fibrosis. Among the clinical disorders associated with silicosis, tuberculosis is by far the most prominent. A 66-year-old male non-smoker, originally from North Africa, reported a dry cough and significant weight loss. He was a foundry worker. He had a medical history of bladder carcinoma associated with schistosomiasis. Computed tomography (CT) and positron emission tomography (PET)/CT showed bilateral multiple hypermetabolic lung nodules, some with cavitation. The patient underwent surgical resection of the largest nodule, which was highly suspicious of lung metastasis. The histological examination revealed multiple nodular formations. Several lesions showed the characteristic features of silicotic nodules. There were also adjacent well-formed granulomas, some with central caseous necrosis. A real-time polymerase chain reaction, performed for the identification and quantification of the DNA of the Mycobacterium tuberculosis complex, was positive. Pulmonary silico-tuberculosis is often encountered in patients with a history of silica exposure in tuberculosis-endemic areas. This case serves as a reminder to never underestimate patient occupational exposure and geographic origin. A careful histological diagnosis and molecular investigation are mandatory when approaching difficult cases, especially patients with a prior cancer history and clinical/radiological features suggestive of tumour recurrence/metastasis

Medición factible del aprendizaje en situaciones de emergencia: enseñanzas de Uganda

Un nuevo instrumento de evaluación ayuda a comprender rápidamente el conjunto de necesidades de los alumnos desplazados

Light absorption properties of southeastern Bering Sea waters: Analysis, parameterization and implications for remote sensing.

The absorption coefficients of phytoplankton (aPHY(λ)), non-algal particles (NAP) (aNAP(λ)) and colored dissolved organic matter (CDOM) (aCDOM(λ)) were investigated and parameterized in the southeastern Bering Sea during July 2008. The absorption coefficients were well structured with respect to hydrographic and biogeochemical characteristics of the shelf. The highest values of aPHY(443) were observed offshore and the lowest values of aPHY(443) were found in the coastal domain, a low productivity region associated with limited macronutrients. Values of aDG(λ) (aCDOM(λ) + aNAP(λ)) revealed an east–west gradient pattern with higher values in the coastal domain, and lower values in the outer domain. Lower chlorophyll specific aPHY(λ) (a*PHY(λ)) observed relative to middle and lower latitude waters indicated a change in pigment composition and/or package effect, which was consistent with phytoplankton community structure. aCDOM(λ) was the dominant light absorbing coefficient at all wavelengths examined except at 676 nm. Modeling of remote-sensing reflectance (Rrs(λ)) and the diffuse attenuation coefficient (Kd(λ)) from inherent optical properties revealed the strong influence of aCDOM(λ) on Rrs(λ) and Kd(λ). Good optical closure was achieved between modeled and radiometer measured Rrs(λ) and Kd(λ) with average percent difference of less than 25% and 19% respectively, except at red wavelengths. The aCDOM(λ) accounted for > 50% of Kd(λ) which was vertically variable. Chlorophyll-a calculated by the NASA standard chlorophyll-a algorithm (OC4.v6) was overestimated due to higher aCDOM(λ) and underestimated due to lower a*PHY(λ) at low and high concentrations of chlorophyll-a, respectively

Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved under- standing of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, inter- rogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain tran- scriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/

Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future

Incomplete annotation has a disproportionate impact on our understanding of Mendelian and complex neurogenetic disorders

Growing evidence suggests that human gene annotation remains incomplete; however, it is unclear how this affects different tissues and our understanding of different disorders. Here, we detect previously unannotated transcription from Genotype-Tissue Expression RNA sequencing data across 41 human tissues. We connect this unannotated transcription to known genes, confirming that human gene annotation remains incomplete, even among well-studied genes including 63% of the Online Mendelian Inheritance in Man–morbid catalog and 317 neurodegeneration-associated genes. We find the greatest abundance of unannotated transcription in brain and genes highly expressed in brain are more likely to be reannotated. We explore examples of reannotated disease genes, such as SNCA, for which we experimentally validate a previously unidentified, brain-specific, potentially protein-coding exon. We release all tissue-specific transcriptomes through vizER: http://rytenlab.com/browser/app/vizER. We anticipate that this resource will facilitate more accurate genetic analysis, with the greatest impact on our understanding of Mendelian and complex neurogenetic disorders