Go Together: Bridging the Gap between Learners and Teachers

After the pandemic, humanity has been facing different types of challenges. Social relationships, societal values, and academic and professional behavior have been hit the most. People are shifting their routines to social media and gadgets, and getting addicted to their isolation. This sudden change in their lives has caused an unusual social breakdown and endangered their mental health. In mid-2021, Pakistan's first Human Library was established under HelpingMind to overcome these effects. Despite online sessions and webinars, HelpingMind needs technology to reach the masses. In this work, we customized the UI or UX of a Go Together Mobile Application (GTMA) to meet the requirements of the client organization. A very interesting concept of the book (expert listener or psychologist) and the reader is introduced in GTMA. It offers separate dashboards, separate reviews or rating systems, booking, and venue information to engage the human reader with his or her favorite human book. The loyalty program enables the members to avail discounts through a mobile application and its membership is global where both the human-reader and human-books can register under the platform. The minimum viable product has been approved by our client organization

SOLID PHASE MICROBIAL FERMENTATION OF ANABOLIC STEROID, DIHYDROTESTOSTERONE WITH ASCOMYCETE FUNGUS FUSARIUM OXYSPORUM

Objective: Microbial catalysis is used in the commercial production of many bioactive steroids. Solid phase microbial fermentation of anabolic steroid, dihydrotestosterone (DHT, 1), was carried out with ascomycete fungal strain Fusarium oxysporum (NRRL-1392).Methods: Sabouraud-4% glucose-agar was used to cultivate the fungal cultures as solid phase medium. Substrate 1 was incubated with Fusarium oxysporum (NRRL-1392) for 8 days. Microbial transformed metabolites were purified by using column chromatographic technique. Results: Ascomycete fungal strain Fusarium oxysporum (NRRL-1392), transformed dihydrotestosterone (1) to four oxidative metabolites 2-5  using solid phase microbial transformation metod. During biotransformation process the hydroxy group was incorporated in inactivated methine carbon atoms at C-7 and C-11 positions. Their structures were elucidated by means of a homo and heteronuclear 2D NMR and by HREI-MS techniques as 17b-hydroxyandrosta-1, 4-dien-3-one 2, androsta-1, 4-diene-3, 17-dione 3, 7a, 17b-dihydroxyandrosta-1, 4-dien-3-one (4), and 11a-hydroxyandrosta-1, 4-diene-3, 17-dione 5. The relative stereochemistry of newly incorporated hydroxy groups were deduced by 2D NOESY experiment.Conclusion: In conclusion, microbial biocatalysis is an attractive alternative tool for the preparation of new bioactive steroids, which might be difficult to prepare by conventional chemical routes. Furthermore, microbial-catalyzed biotransformations can produce commercially valuable steroidal pharmaceuticals for the pharmaceutical industry.Â

RETRACTED: The Heart of Silk Road “Xinjiang,” Its Genetic Portray, and Forensic Parameters Inferred From Autosomal STRs

The Xinjiang Uyghur Autonomous Region of China (XUARC) harbors almost 50 ethnic groups including the Uyghur (UGR: 45.84%), Han (HAN: 40.48%), Kazakh (KZK: 6.50%), Hui (HUI: 4.51%), Kyrgyz (KGZ: 0.86%), Mongol (MGL: 0.81%), Manchu (MCH: 0.11%), and Uzbek (UZK: 0.066%), which make it one of the most colorful regions with abundant cultural and genetic diversities. In our previous study, we established allelic frequency databases for 14 autosomal short tandem repeats (STRs) for four minority populations from XUARC (MCH, KGZ, MGL, and UZK) using the AmpFlSTR® Identifiler PCR Amplification Kit. In this study, we genotyped 2,121 samples using the GoldenEye™ 20A Kit (Beijing PeopleSpot Inc., Beijing, China) amplifying 19 autosomal STR loci for four major ethnic groups (UGR, HAN, KZK, and HUI). These groups make up 97.33% of the total XUARC population. The total number of alleles for all the 19 STRs in these populations ranged from 232 (HAN) to 224 (KZK). We did not observe any departures from the Hardy–Weinberg equilibrium (HWE) in these populations after sequential Bonferroni correction. We did find minimal departure from linkage equilibrium (LE) for a small number of pairwise combinations of loci. The match probabilities for the different populations ranged from 1 in 1.66 × 1023 (HAN) to 6.05 × 1024 (HUI), the combined power of exclusion ranged from 0.999 999 988 (HUI) to 0.999 999 993 (UGR), and the combined power of discrimination ranged from 0.999 999 999 999 999 999 999 983 (HAN) to 0.999 999 999 999 999 999 999 997 (UGR). Genetic distances, principal component analysis (PCA), STRUCTURE analysis, and the phylogenetic tree showed that genetic affinity among studied populations is consistent with linguistic, ethnic, and geographical classifications

SOLID PHASE MICROBIAL REACTIONS OF SEX HORMONE, TRANS-ANDROSTERONE WITH FILAMENTOUS FUNGI

Objective: A microbial biotransformation study was performed on trans-androsterone (1) using solid phase medium. In the present context, trans-androsterone (1), a sex hormone was fermented with two filamentous fungi, Rhizopus stolonifer (black bread mold) and Fusarium lini.Methods: Sabouraud-4% glucose-agar were used to cultivate the fungal cultures as solid phase medium. Substrate 1 was incubated with R. stolonifer (ATCC 10404) and F. lini (NRRL 68751) for 8 days. Microbial transformed metabolites were purified by using column chromatographic technique. Results: The metabolism study of 1 revealed that various metabolites were detected when incubated with filamentous fungi. A total of 3 transformed products were obtained. The reactions occurred that exhibited diversity; including selective hydroxylation at C-6 and C-7 along with oxidation occurs at C-3 positions. Their structure and identified on the basis of extensive spectroscopic data (NMR, HREIMS, IR and UV) as 3b,7b-dihydroxy-5a-androstan-17-one 2 in a good yield (58%), 6b-hydroxy-5a-androstan-3,17-dione 3, and 3b,6b-dihydroxy-5a-androstan-17-one 4.Conclusion: Solid phase microbial transformation method can successfully be used for the development of new steroidal drugs. The modified steroidal molecules could favor when compared to their natural counterparts due to several medicinal advantages.Â

A review of biophysiological and biochemical indicators of stress for connected and preventive healthcare

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies

R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild

Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ignoring unstructured roadways containing distress, potholes, water puddles, and various kinds of road patches i.e., earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset (R2S100K) -- a large-scale dataset and benchmark for training and evaluation of road segmentation in aforementioned challenging unstructured roadways. R2S100K comprises 100K images extracted from a large and diverse set of video sequences covering more than 1000 KM of roadways. Out of these 100K privacy respecting images, 14,000 images have fine pixel-labeling of road regions, with 86,000 unlabeled images that can be leveraged through semi-supervised learning methods. Alongside, we present an Efficient Data Sampling (EDS) based self-training framework to improve learning by leveraging unlabeled data. Our experimental results demonstrate that the proposed method significantly improves learning methods in generalizability and reduces the labeling cost for semantic segmentation tasks. Our benchmark will be publicly available to facilitate future research at https://r2s100k.github.io/