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This study establishes the applicability of various machine learning (ML) algorithms for creating in silico models based on data from historical bioprocess development and manufacturing activities. We demonstrate this by developing ML-based models for a particular case of predicting viral clearance performance of anion exchange (AEX) chromatography operated in flow-through (FT) mode, a processing step that is essential for ensuring viral safety of biotherapeutics and that requires significant resources due to a large number of expensive wet lab design and characterization experiments. Using these ML-based models, we then evaluate the impact of various feed-, virus-, and process-related parameters (such as the molecular attributes of the product, the type of chromatography matrix, loading capacity, equilibration/wash buffer pH and conductivity, virus type and load, etc.) on the viral clearance performance and hence generate process understanding essential for developing FT-AEX chromatography processes with robust viral clearance performance. Finally, we show that ML-based optimization methods can be used to determine process operating ranges for improved viral clearance performance. These results are significant as they provide approaches and understanding necessary for faster and cheaper development of robust FT-AEX chromatography-based viral clearance processes and generally provide guidance on the implementation of ML-based prediction and optimization approaches in bioprocessing - crucial to reducing costs and time associated with the design and manufacturing of biotherapeutics and making these treatments widely accessible.

Inferior vena cava (IVC) injuries account for about 25%–40% of abdominal vascular injuries. Blunt vena cava injury has a lower survival rate than penetrating injury. They may present with torrential bleed. In extreme cases, ligation of the IVC may have to be done as a damage control procedure. However, it is associated with significant morbidity and mortality, especially when the suprarenal IVC is ligated. It can lead to acute kidney injury and lower limb compartment syndrome. Suprarenal IVC ligation has been done in oncological resections, as it is a chronic disease that allows collateral vessel formation. However, there is a paucity of data on the ligation of the suprarenal IVC in acute trauma. We present a case of penetrating injury to the IVC which was managed initially by suprarenal IVC ligation, followed by synthetic interposition graft placement once the patient stabilized hemodynamically. The case highlights that this approach can be viable as a damage control procedure in exsanguinating patients.

Abstract. Pancreatic trauma remains a challenge to a trauma surgeon even with extensive advancement in medicine. The mortality and morbidity associated with this injury remain high. A high index of suspicion, mechanism of injury, and early identification hold key in the final outcome. The management of pancreatic injury should involve a multidisciplinary team comprising of trauma surgeons, radiologists, and gastroenterologists. This review highlights the burden of the pancreatic injury and its management.

Penetrating trauma is one of the most common cause of occult vascular injury, especially in low velocity penetrating injury. This is usually in pretext of an innocuous looking small external wound. It is important for clinician to have high index of suspicion for such types of injuries, as the clinical examination with presenting signs and symptoms may be deceptive. Radiological evaluation is of paramount importance and essential to rule out any occult vascular insult. Conventional open surgical exploration is the gold standard for vascular injuries in neck. Minimal invasive endovascular stenting has selective role based on zone of neck, mechanism of injury, hemodynamic stability, neurological status and resource availability at treating centre. Early diagnosis and referral to an appropriate higher centre, preferably a trauma centre with expertise in handling major vascular injuries, improve the outcome. We present a case report of small innocuous looking external wound harbouring major central vessel injury underneath following low-velocity penetrating injury.

In this paper, an analytical model of threshold voltage for bulk MOSFET is developed. The model is derived from the physical charge-based core of BSIM6 MOSFET model, taking into account short channel effects, and is intended to be used in commercial SPICE simulators for operating point information. The model is validated with measurement data from IBM 90-nm technology node using various popular threshold voltage extraction techniques, and good agreement is obtained.

An improved analytical model for flicker noise (1/f noise) in MOSFETs is presented. Current models do not capture the effect of high-trap density in the halo regions of the devices, which leads to significantly different bias dependence of flicker noise across device geometry. The proposed model is the first compact model implementation capturing such effect and show distinct improvements over other existing noise models. The model is compatible with BSIM6, the latest industry standard model for bulk MOSFET, and is validated with measurements from 45-nm low-power CMOS technology node.

Cepstral filtering technique is applied on an interlaced image, the pattern similar to that which is found in layer IV of Primate Visual Cortex. It involves Power spectrum in computation, which is square of absolute of Fast Fourier Transform (FFT), is a complicated and hardware unfriendly. We propose an algorithm in which Gabor filters, instead of Power Spectrum, are applied to an interlaced image in the Cepstral algorithm. This scheme makes it hardware friendly as it gives the flexibility of working with modules which can be imitated in hardware. Building a FFT module is a tough task in analog circuit but determining Gabor energy, an alternative to it, can be achieved by elementary circuits. The Phase, Energy Models and other methods, use multi-lambda Gabor filters to compute disparity. The proposed method uses sum of absolute difference to choose a single Gabor filter of appropriate lambda that fits to find the disparity. The algorithm inherits the quality of both Gabor filter and Ocular Dominance Pattern and hence a biologically inspired and suitable for hardware realization. The proposed algorithm has been implemented on the test data image. A hardware scheme has also been proposed that can be used to estimate disparity and the idea can be extended in building complex modules that can perform real time - real image operations with a handful of resources as compared to employing complex digital FPGAs and CPLDs.