Formal Milk Processing Sector in Assam: Lessons to be Learnt from Institutional Failure

Assam initiated organised development of milk processing way back in the mid 1960s. The total installed capacity of pasteurisation and chilling plants in the State is 159 thousand and 28.5 thousand litres per day, respectively. The current scenario of the formal milk processing segment in the state is however, grim. The created infrastructure is either largely defunct or grossly under-utilized. The functional plants are operating at very low level of their installed capacity, have limited product profile, high returns of marketed milk, substantial handling and curdling losses, low productivity of capital and labour and huge operational losses. The poor performance of the plants has been attributed to the establishment of milk processing units without an appropriate assessment of output demand and input supply and ascertainment of economic viability of the plants. In addition, the supporting institutional and infrastructural mechanism has not been put in place and a systematic business and management plan to run the system has not been formulated. Drawing lessons from the institutional failure, the study has suggested some possible interventions and policy initiatives for strengthening the dairy processing activities in the state of Assam.Agricultural and Food Policy,

Biomimetic mechanism for micro aircraft

A biomimetic pitching and flapping mechanism including a support member, at least two blade joints for holding blades and operatively connected to the support member. An outer shaft member is concentric with the support member, and an inner shaft member is concentric with the outer shaft member. The mechanism allows the blades of a small-scale rotor to be actuated in the flap and pitch degrees of freedom. The pitching and the flapping are completely independent from and uncoupled to each other. As such, the rotor can independently flap, or independently pitch, or flap and pitch simultaneously with different amplitudes and/or frequencies. The mechanism can also be used in a non-rotary wing configuration, such as an ornithopter, in which case the rotational degree of freedom would be suppressed

Zika Virus Can Strongly Infect and Disrupt Secondary Organizers in the Ventricular Zone of the Embryonic Chicken Brain

Zika virus (ZIKV) is associated with severe neurodeve- lopmental impairments in human fetuses, including microencephaly. Previous reports examining neural progenitor tropism of ZIKV in organoid and animal models did not address whether the virus infects all neural progenitors uniformly. To explore this, ZIKV was injected into the neural tube of 2-day-old chicken embryos, resulting in nonuniform periventricular infec- tion 3 days later. Recurrent foci of intense infection were present at specific signaling centers that influ- ence neuroepithelial patterning at a distance through secretion of morphogens. ZIKV infection reduced transcript levels for 3 morphogens, SHH, BMP7, and FGF8 expressed at the midbrain basal plate, hypotha- lamic floor plate, and isthmus, respectively. Levels of Patched1, a SHH-pathway downstream gene, were also reduced, and a SHH-dependent cell popula- tion in the ventral midbrain was shifted in position. Thus, the diminishment of signaling centers through ZIKV-mediated apoptosis may yield broader, non- cell-autonomous changes in brain patterning

Determination and expression of genes for resistance to blast (Magnaporthe oryza) in Basmati and non-Basmati indica rices (Oryza sativa L.)

One hundred and twenty two (122) genotypes of Basmati and non-Basmati Indica rice genotypes were evaluated for expression of resistance against blast disease under induced epiphytotic conditions. Disease severity (%) and area under disease progress curve (AUDPC) parameters were used for screening the blast resistance. Only 13 genotypes expressed resistance against the blast disease. Nine genotypes carried blast resistance genes but, were susceptible under induced epiphytotic conditions. The rice genotype VLD-61 had no resistance genes; however, it expressed strong resistance against blast. An empirical breeding strategy for development of blast resistant improved varieties of rice was also discussed.Keywords: Magnaporthe oryzae, restriction digestion, molecular breeding, Basmati riceAfrican Journal of Biotechnology Vol. 12(26), pp. 4098-410

Rethinking neoadjuvant chemotherapy for breast cancer

Breast cancer is the most common cancer in women worldwide. In 2014, 55 000 women in the UK were given the diagnosis of breast cancer, and 11 000 died.1 Early breast cancer is traditionally treated with surgery, plus radiotherapy and adjuvant systemic therapy as required. Neoadjuvant chemotherapy for breast cancer is a new strategy that was introduced towards the end of the 20th century with the aim of reducing tumour size. It has four main rationales. Firstly, it should render an otherwise inoperable tumour operable or, secondly, allow more conservative surgery. Thirdly, starting systemic treatment preoperatively was hoped to lead to improved overall survival in patients with locally advanced cancers, who are at high risk of having distant disease. Finally, unlike adjuvant chemotherapy given in the absence of any measurable disease, neoadjuvant chemotherapy gives us the opportunity to observe the tumour shrink both palpably and on imaging, enabling a rapid assessment of clinical response. This could help test responses in vivo to new drug regimens, which could then be used as adjuvant therapies, in so called window of opportunity studies. A survey of multidisciplinary teams in Australia, Germany, Italy, the UK, and the US found that 7-27% of new breast cancers are treated with neoadjuvant chemotherapy (Saunders C, Cody H, Kolberg HC, et al, personal communication, 2017). With 1.7 million women receiving diagnoses annually, this translates into 120 000-460 000 women receiving neoadjuvant chemotherapy worldwide.1 Although data indicate that the first rationale remains valid, the others have not led to the desired outcomes. More conservative surgery after neoadjuvant chemotherapy can result in a higher rate of local recurrence, and, despite the earlier initiation of systemic treatment, no improvement in survival has been seen.234 Furthermore, neoadjuvant chemotherapy may not help test novel chemotherapies—although primary tumour response is a good indicator of prognosis for a particular treatment, it is counterintuitively a poor surrogate marker for the overall survival benefit when evaluating novel chemotherapy regimens. Finally, for 40-80% of patients, even the best neoadjuvant chemotherapy regimens extend the period the cancer remains in the breast and can make surgery more difficult, as the tumour is less easily palpable and the axillary lymph nodes are less distinct. We question the wisdom of the current widespread use of neoadjuvant chemotherapy

Novel applications of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) in the analysis of ultrafast electron diffraction (UED) images

We employ generative adversarial networks (GANs) and convolutional neural networks (CNNs) in the study of ultrafast electron diffraction images. We propose a machine learning approach that employs a GAN to convert experimental images into idealized diffraction patterns from which information is extracted via a CNN trained on synthetic data only. We validate this approach on ultrafast electron diffraction (UED) data of bismuth samples undergoing thermalization upon excitation via 800 nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples

EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning

Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decision making and path planning. In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework using evidential learning to directly estimate both classification and regression uncertainties. To employ evidential learning for object detection, we devise a combination of evidential and focal loss functions for the sparse heatmap inputs. We introduce class-balanced weighting for regression and heatmap prediction to tackle the class imbalance encountered by evidential learning. Moreover, we propose a learning scheme to actively utilize the predicted heatmap uncertainties to improve the detection performance by focusing on the most uncertain points. We train our model on the KITTI dataset and evaluate it on challenging out-of-distribution datasets including BDD100K and nuImages. Our experiments demonstrate that our approach improves the precision and minimizes the execution time loss in relation to the base model