Milk Phospholipids Extraction Using Switchable Solvents

Phospholipids (PLs) found in milk are of great interest due to their health and nutritional benefits associated with their consumption. By-products generated during the manufacture of dairy products represent a source of PLs with potential for value-added opportunities. Currently, the extraction of PLs from by-products results in overall lowefficiencies, and it involves subsequent solvent separation and lipid recovery. This dissertation studied the extraction of milk PLs from by-product streams with a tertiary anime (N, N-dimethyl cyclohexylamine, CyNMe2) as a switchable hydrophilicity solvent. This type of solvents can be reversibly switched between a hydrophobic and hydrophilic form in the presence or absence of CO2. Different dairy by-products streams, including buttermilk (BM), beta-serum (BS), concentrate buttermilk (CBM), and raw cream (RC) was used to evaluate the feasibility of CyNMe2. The extraction efficiency of CyNMe2 ranged from 0.33-99%, depending on the type of byproduct. Remarkably, CyNMe2 extracted up to 99% of the PLs directly from buttermilk. The extraction of PLs from BS was further studied using ultrasound prior to CyNMe2 extraction. Overall, higher levels of acoustic intensity (44.56 ± 3.47 W cm-2) prior to CyNMe2 extraction recovered 69.07 ± 0.11% of PLs, 10-fold higher than the samples without ultrasound pretreatment. The recovered fraction of PLs mainly comprised of phosphatidylinositol (32%), phosphatidylethanolamine (30%), and sphingomyelin 37%). The effect of the extraction temperature (25, 40, and 60oC), time (3, 10, and 18 h) and solvent ratio (3/1, 10/1, and 18/1 mL) was also studied. The highest yield obtained was 29.29 ± 0.06% of PLs at 60 oC, at minimized solvent ratio and time (3/1 mL and 3 h, respectively). At these conditions, the fraction of PLs recovered was phosphatidylinositol (59%) and phosphatidylethanolamine (35%). Insights into the extraction mechanism of the CyNMe2 was studied through various analytical measurements, such as analysis of protein-profile, particle size, zeta potential, and microstructure, Confocal Laser Scanning Microscopy (CLSM) and Scanning Electron Microscopy (SEM). CyNMe2 seems to disrupt the protein-membrane through ion pair formation, releasing the PLs into the aqueous medium. Throughout this thesis, CyNMe2 shows to be an effective way to concentrate PLs from by-products

AN ASSESSMENT OF LEAD AND COPPER IN SCHOOL DRINKING WATER

Metal contamination in drinking water, especially heavy metals such as lead, and corrosion issues, pose a threat to human health and have received increasing attention in recent years. This study reports on the results of ‘The Massachusetts Assistance Program for Lead in School Drinking Water’ and provides a better understanding of the dynamics of sampling protocols, premise plumbing and their influence on lead and copper concentrations. ‘The Massachusetts Assistance Program for Lead in School Drinking Water’ that was announced in April 2016, implemented tap-based water sampling for lead and copper at K-12 public schools and Early Education and Childcare centers in Massachusetts, thus targeting the most vulnerable populations to the health risks of lead and copper exposure. The Program funded by the Massachusetts Department of Environmental Protection (MassDEP) and implemented by both UMass Amherst and MassDEP, saw the collection and testing of about 68,000 water samples across 992 school buildings between June 2016 and December 2018. About 44% of school buildings had one or more samples exceeding the lead AL, 9% of school buildings had one or more samples exceeding the copper AL, and another 29% had exceedances of both the lead and copper ALs. Flushing was found to have a higher impact in decreasing lead concentrations as compared to the impact on copper concentrations. A decrease in lead concentration levels is seen in schools constructed later than the year 1990, possibly indicating the implementation of lead materials control following the regulations in the 1980s and the LCR

Septicaemia in pregnancy: a case report

Necrotizing fasciitis (NF) is a rare, life-threatening surgical infection in pregnancy with high rates of morbidity and mortality. It is a severe, potentially fatal infectious disease which rapidly extends from the subcutaneous tissue along the superficial and deep fascia causing vascular occlusion, ischemia, and necrosis of tissues. A 30-year-old gravida 2 para1 living 1 woman, at 32 weeks of gestation with previous caesarean section and recently diagnosed diabetes, hypertension was admitted to our hospital with signs and symptoms of severe sepsis with pruritic black lesions over abdomen and perineum. Patient was in a morbid state in our hospital. During clinical examination, fetal heart sound was not localised suggestive of intrauterine fetal demise (IUFD), with ulcerative lesions over abdomen and vulva. Patient was immediately taken for surgical intervention and was suggestive of ruptured uterus with extrusion of fetus in abdominal cavity with cellulitis of abdominal and vulva. During initial laboratory examinations, diabetes mellitus was diagnosed. Patient was kept on ventilatory support and was vitals were stabilised. Multidisciplinary therapy with immediate aggressive surgical debridement of necrotic tissues, multiple antibiotics, and intensive care monitoring was performed successfully. The patient’s postoperative course was uncomplicated and skin defect healed by second intention of healing. The following case emphasized the potential immunosuppressive role of pregnancy state in conjunction with diabetes mellitus in the development of severe necrotizing soft tissue infections

A laparoscopic management of Swyer syndrome in a woman with pure 46XY gonads and hypoplastic dysgenesis-a rare presentation

Swyer syndrome is also known as complete/pure gonadal dysgenesis & is associated with an absence of testicular differentiation in a phenotypic female with a 46, XY karyotype. A 23-year-old married girl had come with complaints of primary infertility. She was averagely built & nourished. She had well developed secondary sexual characteristics like  breast development,  axillary hairs, and  pubic hairs (Tanners stage 4) secondary to receiving HRT for getting  menses. External genitalia was of female type. Karyotype showed genotype of 46, XY. Magnetic resonance imaging revealed hypoplastic uterus with rudimentary fallopian tubes , and fibrotic gonad like tissue. A diagnosis of Swyer syndrome was made. The patient was started on hormonal replacement therapy. Dysgenetic streak gonads were removed laparoscopically . Pathologic examination revealed seminiferous tubules ,rete testis & epididymis without evidence of malignant transformation. The patient tolerated the procedure well . Thus laparoscopic prophylactic gonadectomy revealed to be an economical ,straightforward and simple procedure in this patient & risk of neoplasia could be prevented. 

Tailoring fusion-based error correction for high thresholds to biased fusion failures

We introduce fault-tolerant (FT) architectures for error correction with the XZZX cluster state based on performing measurements of two-qubit Pauli operators $Z\otimes Z$ and $X\otimes X$, or fusions, on a collection of few-body entangled resource states. Our construction is tailored to be effective against noise that predominantly causes faulty $X\otimes X$ measurements during fusions. This feature offers practical advantage in linear optical quantum computing with dual-rail photonic qubits, where failed fusions only erase $X\otimes X$ measurement outcomes. By applying our construction to this platform, we find a record high FT threshold to fusion failures exceeding $25\%$ in the experimentally relevant regime of non-zero loss rate per photon, considerably simplifying hardware requirements.Comment: 7+6 pages, 4+6 figures, comments welcom

Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving

This short paper presents a preliminary analysis of three popular Visual Question Answering (VQA) models, namely ViLBERT, ViLT, and LXMERT, in the context of answering questions relating to driving scenarios. The performance of these models is evaluated by comparing the similarity of responses to reference answers provided by computer vision experts. Model selection is predicated on the analysis of transformer utilization in multimodal architectures. The results indicate that models incorporating cross-modal attention and late fusion techniques exhibit promising potential for generating improved answers within a driving perspective. This initial analysis serves as a launchpad for a forthcoming comprehensive comparative study involving nine VQA models and sets the scene for further investigations into the effectiveness of VQA model queries in self-driving scenarios. Supplementary material is available at https://github.com/KaavyaRekanar/Towards-a-performance-analysis-on-pre-trained-VQA-models-for-autonomous-driving

Position-Dependent Codon Usage Bias in the Human Transcriptome

All life depends on the reliable translation of RNA to protein according to complex interactions between translation machinery and RNA sequence features. While ribosomal occupancy and codon frequencies vary across coding regions, well-established metrics for computing coding potential of RNA do not capture such positional dependence. Here, we investigate position-dependent codon usage bias (PDCUB), which dynamically accounts for the position of protein-coding signals embedded within coding regions. We demonstrate the existence of PDCUB in the human transcriptome, and show that it can be used to predict translation-initiating codons with greater accuracy than other models. We further show that observed PDCUB is not accounted for by other common metrics, including position-dependent GC content, consensus sequences, and the presence of signal peptides in the translation product. More importantly, PDCUB defines a spectrum of translational efficiency supported by ribosomal occupancy and tRNA adaptation index (tAI). High PDCUB scores correspond to a tAI-defined translational ramp and low ribosomal occupancy, while low PDCUB scores exhibit a translational valley and the highest ribosomal occupancy. Finally, we examine the relationship between PDCUB intensity and functional enrichment. We find that transcripts with start codons showing the highest PDCUB are enriched for functions relating to development of musculoskeletal, cardiovascular, neurological, gastrointestinal, sensory, and other body systems. Furthermore, transcripts with high PDCUB are depleted for functions related to immune response and detection of chemical stimulus. These findings lay important groundwork to improve our understanding of the regulation of translation, the calculation of coding potential, and the classification of RNA transcripts

Mapping the genotype-phenotype relationship in complex disease.

Computational methods to identify harmful variations in humans perform well for rare diseases such as Huntington\u27s but not for common diseases like hypertension or diabetes. A modelling approach that takes protein context into account was illustrated to identify harmful variants involved in complex diseases

Minimising surface-code failures using a color-code decoder

The development of practical, high-performance decoding algorithms reduces the resource cost of fault-tolerant quantum computing. Here we propose a decoder for the surface code that finds low-weight correction operators for errors produced by the depolarising noise model. The decoder is obtained by mapping the syndrome of the surface code onto that of the color code, thereby allowing us to adopt more sophisticated color-code decoding algorithms. Analytical arguments and exhaustive testing show that the resulting decoder can find a least-weight correction for all weight $d/2$ depolarising errors for even code distance $d$. This improves the logical error rate by an exponential factor $O(2^{d/2})$ compared with decoders that treat bit-flip and dephasing errors separately. We demonstrate this improvement with analytical arguments and supporting numerical simulations at low error rates. Of independent interest, we also demonstrate an exponential improvement in logical error rate for our decoder used to correct independent and identically distributed bit-flip errors affecting the color code compared with more conventional color-code decoding algorithms

Detection of Abuse in Financial Transaction Descriptions Using Machine Learning

Since introducing changes to the New Payments Platform (NPP) to include longer messages as payment descriptions, it has been identified that people are now using it for communication, and in some cases, the system was being used as a targeted form of domestic and family violence. This type of tech-assisted abuse poses new challenges in terms of identification, actions and approaches to rectify this behaviour. Commonwealth Bank of Australia's Artificial Intelligence Labs team (CBA AI Labs) has developed a new system using advances in deep learning models for natural language processing (NLP) to create a powerful abuse detector that periodically scores all the transactions, and identifies cases of high-risk abuse in millions of records. In this paper, we describe the problem of tech-assisted abuse in the context of banking services, outline the developed model and its performance, and the operating framework more broadly.Comment: 7 pages, 3 figure