Microbiomics of Populus

Species of the genus Populus, commonly known as poplars, are one of the most widely used groups of forest trees in North America and Europe. Poplars play a significant ecological role as a pioneer species in the boreal forest, and as a dominant species in riparian forests where they serve as rich wildlife habitats. Numerous natural and artificial hybrids of poplars with superior qualities are widely used in commercial plantations. However, many hybrid poplars are susceptible to the leaf spot and stem canker causative pathogenic fungal species, Sphaerulina musiva which limits the utility of hybrid poplars as plantation trees. At present, there is no control measure to combat the disease caused by S. musiva. The plant microbiome—mainly endophytic microbes—are known to interact with pathogenic microbes and play a crucial role in the ecology and evolution of plants. Understanding the endophytic microbial associations in Poplars along with their interactions with S. musiva is invaluable for developing new methods for combating the stem cankers caused by this pathogen. In the present study, we found a number of endophytic microbes in Poplars that inhibit the fungal pathogen S. musiva and may serve as a potential source to develop biocontrol agents against S. musiva. In the first study (Chapter 2), we characterized the endophytic fungal community in Populus species in the Gault Nature Reserve in Mont-Saint-Hilaire, Quebec. We investigated the dual culture interactions of endophytes against S. musiva. We isolated 367 endophytic fungal isolates grouped into 46 genera. Alternaria was the dominant and most common genus isolated. We found certain genera of endophytic fungi were unique to specific Populus species. Only a few endophytic fungi exhibited antagonistic activity against S. musiva and showed differential competitive ability. The endophytic fungus Fusarium sporotrichioides showed the strongest antagonistic activity against S. musiva and can be used as a potential source to develop a biocontrol microbe against S. musiva. In the second study (Chapter 3), we investigated interactions between Bacillus velezensis EB14, an endophytic bacterial strain isolated from poplars, and S. musiva. We found significant inhibition of S. musiva by the endophytic bacterium, B. velezensis. We also discovered the production of anti-fungal Iturin compounds (iturin A1, subtulene A, iturin A2, iturin A9 and fengycin) along with four unknown compounds in Bacillus velezensis. In addition, we found that B. velezensis EB14 exhibited varying level of competitive ability against the endophytic fungal microbiome of Populus. In the third study (Chapter 4), we elucidated the evolutionary relationships of the isolated Bacillus strain EB14, and performed a comparative genome analysis of the Bacillus velezensis EB14 strain with its closest relatives. We report the 4.07Mbp draft genome of Bacillus velezensis EB14. This genome encodes 12 secondary metabolite gene clusters which includes Surfactin, Rhizoactin, Bacillibactin, Fengycin, Bacillaene, Difficidin, Macrolactin, and Bacilysin. The presence of genes involved in plant bacterial interactions further validates the potential of using Bacillus velezensis EB14 strain to develop as a biocontrol microbe in forestry and agriculture. Furthermore, pan and core genome analysis revealed that Bacillus strains associated with plants possessed genes involved in cell wall degradation, polyketide synthesis, and environment sensors. Most of these genes were clade specific and found in B. amyloliquefaciens, B. siamensis, and the conspecific group of B. velezensis. The last chapter (Chapter 5), examines one of the most perplexing questions in endophyte biology: How and why do endophytes produce metabolites similar to host plant derived secondary metabolites? Here, we review the endophyte literature on secondary metabolite production and show that detailed studies are required for conclusive demonstration of metabolite production, as well as to explain the adaptive significance of production of these metabolites by endophytic fungi

Convolution Neural Network Model for Recognition of Speech for Words used in Mathematical Expression

Speech recognition is translation of audio signal into human readable form. Speech recognition plays a vital role in various areas such as in signal processing, dictation system, command and control, simple data entry.  Speech recognition in dictation system helps the disabled people. In this paper authors have performed the experiment for speech recognition of   mathematical words which is helpful to disabled people. Now a day’s the use of deep learning in various applications is challenging for the improvement of model. In this paper authors have used CNN model to improve the recognition accuracy. Authors have selected 17 mathematical words which are the most commonly used in mathematical expression. Rectified Linear unit activation function is used to train the CNN because of its fast computation. This paper evaluates the model for MFCC and Delta MFCC features for Adam and Adagrad optimizers. Result shows that Delta MFCC gives an accuracy of 83.33 % for both Adam and Adagrad optimizer. It indicates that Delta MFCC gives better result than MFCC. Result also shows that Adagrad with Delta MFCC trains the model earlier than Adam

Tricking LLMs into Disobedience: Understanding, Analyzing, and Preventing Jailbreaks

Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating the prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies. Limited formal studies have been carried out to formalize and analyze these attacks and their mitigations. We bridge this gap by proposing a formalism and a taxonomy of known (and possible) jailbreaks. We perform a survey of existing jailbreak methods and their effectiveness on open-source and commercial LLMs (such as GPT 3.5, OPT, BLOOM, and FLAN-T5-xxl). We further propose a limited set of prompt guards and discuss their effectiveness against known attack types

Multi-task Learning for Speaker Verification and Voice Trigger Detection

Automatic speech transcription and speaker recognition are usually treated as separate tasks even though they are interdependent. In this study, we investigate training a single network to perform both tasks jointly. We train the network in a supervised multi-task learning setup, where the speech transcription branch of the network is trained to minimise a phonetic connectionist temporal classification (CTC) loss while the speaker recognition branch of the network is trained to label the input sequence with the correct label for the speaker. We present a large-scale empirical study where the model is trained using several thousand hours of labelled training data for each task. We evaluate the speech transcription branch of the network on a voice trigger detection task while the speaker recognition branch is evaluated on a speaker verification task. Results demonstrate that the network is able to encode both phonetic \emph{and} speaker information in its learnt representations while yielding accuracies at least as good as the baseline models for each task, with the same number of parameters as the independent models

Diaphyseal fractures of the forearm in adults, comparative study of dynamic compression plate versus intramedullary nail

Background: Forearm fractures are common nowadays because of road traffic accident. It is important to achieve anatomical reduction of both bone forearm fractures to regain function of upper limb. This study is undertaken to observe functional and radiological outcome using two different surgical modalities like dynamic compression plating (DCP), and intramedullary nailing in both bone forearm fractures and also to indivualize the optimal treatment method for different fracture pattern.Methods: Our study included 60 patients with diaphyseal forearm fractures in adults presenting to orthopaedic outpatient department. Among 60 patients, 30 patients underwent open reduction and internal fixation by dynamic compression plate and other 30 patients underwent closed reduction/open reduction by square nail after detailed pre-operative evaluation.Results: In our study average union time in DCP group is 23.39 weeks and square nail group is 28.89 weeks. Union in DCP group was 27 (90%) and square nail group 22 (73.33%). Delayed union in DCP group was 03 (10%) and in Square nail group was 6 (20%), non-union in DCP group was 0 (nil) and in square nail group was 2 (06%).Conclusions: Open reduction and internal fixation with DCP plates for both bone diaphyseal forearm fractures gives good results with early union rates. We also found that in open fractures and complex fracture like segmental fractures square nailing was better option compared to dynamic compression plate to reduce infection rates, retain periosteal blood supply from soft tissue. Thus we conclude that both implants are equally important and we should prioritize based on preoperative planning

Weight subcloning: direct initialization of transformers using larger pretrained ones

Training large transformer models from scratch for a target task requires lots of data and is computationally demanding. The usual practice of transfer learning overcomes this challenge by initializing the model with weights of a pretrained model of the same size and specification to increase the convergence and training speed. However, what if no pretrained model of the required size is available? In this paper, we introduce a simple yet effective technique to transfer the knowledge of a pretrained model to smaller variants. Our approach called weight subcloning expedites the training of scaled-down transformers by initializing their weights from larger pretrained models. Weight subcloning involves an operation on the pretrained model to obtain the equivalent initialized scaled-down model. It consists of two key steps: first, we introduce neuron importance ranking to decrease the embedding dimension per layer in the pretrained model. Then, we remove blocks from the transformer model to match the number of layers in the scaled-down network. The result is a network ready to undergo training, which gains significant improvements in training speed compared to random initialization. For instance, we achieve 4x faster training for vision transformers in image classification and language models designed for next token prediction

Comparison of knowledge, attitude and concern about HIV/AIDS patients among dental students: A cross sectional survey

HIV/AIDS has taken a pandemic form affecting 40 million people around the world. The present study aimed to determine the knowledge, attitude, and concerns of dental students towards HIV/AIDS infected individuals. A cross sectional study was conducted among 224 subjects, among them 112 final year (FY) students and 112 interns. Subjects were selected from 10 dental colleges in Bangalore city, India. Data was collected through a self-administered questionnaire. The mean knowledge score of FY students and interns was 73.66+5.9 and 80.4+7.2 respectively; the mean attitude score was 71.25+1.707 and 87.75+1.8 and the mean concern score was 92+2.645 and 97.75+3.171 respectively. Differences in the mean score were significant. Dental interns had slightly higher knowledge, attitude, and concern than the FY students. There is a need to add HIV/AIDS patient’s infection control measures in the dental curriculum.   Le VIH/SIDA a pris une forme pandémique touchant 40 millions de personnes dans le monde. La présente étude visait à déterminer les connaissances, l'attitude et les préoccupations des étudiants en médecine dentaire envers les personnes infectées par le VIH/SIDA. Une étude transversale a été menée auprès de 224 sujets, dont 112 étudiants de dernière année (FY) et 112 stagiaires. Les sujets ont été sélectionnés dans 10 collèges dentaires de la ville de Bangalore, en Inde. Les données ont été recueillies au moyen d'un questionnaire auto-administré. Le score de connaissance moyen des étudiants et des stagiaires FY était respectivement de 73,66+5,9 et 80,4+7,2 ; le score moyen d'attitude était de 71,25+1,707 et 87,75+1,8 et le score moyen de préoccupation était respectivement de 92+2,645 et 97,75+3,171. Les différences dans le score moyen étaient significatives. Les stagiaires dentaires avaient des connaissances, une attitude et des préoccupations légèrement plus élevées que les étudiants de l'AF. Il est nécessaire d'ajouter les mesures de contrôle de l'infection des patients atteints du VIH/SIDA dans le programme d'études dentaires

Effect of ECAE Die Angle on Microstructure Mechanical Properties and Corrosion Behavior of AZ80/91 Magnesium Alloys

Magnesium alloys have poor tensile strength, ductility and corrosion resistance properties associated with other engineering materials like aluminum alloys, steels and superalloys etc. Therefore, many researchers worked on equal channel angular pressing of magnesium alloys to improve the mechanical properties and corrosion resistance. In this work, the effect of channel angles on material properties was investigated during equal channel angular pressing of AZ80/91 magnesium alloy using processing route-R at 598 K processing temperature. Channel angles of 900 and 1100, common corner angle of 300 have been considered for the study. It has been revealed that the channel angle has a significant influence on deformation homogeneity, microhardness, ultimate tensile strength, ductility, and corrosion behavior of AZ80/91 magnesium alloys. Specifically, AZ80/91 Mg alloys processed through 900 channel angle i.e. die A is considered as optimal die parameter to improve above-said material properties. Investigation showing concerning as-received AZ80 and AZ91 Mg alloy indicates 11%, 14% improvement of UTS and 69%, 59% enhancement in ductility after processing through 4P through die A (90°) at 598 K respectively. Also, the corrosion rate reduces to 97% and 99% after processing the sample with 4P-ECAP die A (90°) at the same processing temperature for AZ80 and AZ91 Mg alloys respectively. This is mainly due to grain refinement and distribution of Mg17Al12 secondary phase during ECAP